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Smith J, Qiao Y, Williams AL. Evaluating the utility of identity-by-descent segment numbers for relatedness inference via information theory and classification. G3 (BETHESDA, MD.) 2022; 12:jkac072. [PMID: 35348675 PMCID: PMC9157175 DOI: 10.1093/g3journal/jkac072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 03/07/2022] [Indexed: 11/29/2022]
Abstract
Despite decades of methods development for classifying relatives in genetic studies, pairwise relatedness methods' recalls are above 90% only for first through third-degree relatives. The top-performing approaches, which leverage identity-by-descent segments, often use only kinship coefficients, while others, including estimation of recent shared ancestry (ERSA), use the number of segments relatives share. To quantify the potential for using segment numbers in relatedness inference, we leveraged information theory measures to analyze exact (i.e. produced by a simulator) identity-by-descent segments from simulated relatives. Over a range of settings, we found that the mutual information between the relatives' degree of relatedness and a tuple of their kinship coefficient and segment number is on average 4.6% larger than between the degree and the kinship coefficient alone. We further evaluated identity-by-descent segment number utility by building a Bayes classifier to predict first through sixth-degree relationships using different feature sets. When trained and tested with exact segments, the inclusion of segment numbers improves the recall by between 0.28% and 3% for second through sixth-degree relatives. However, the recalls improve by less than 1.8% per degree when using inferred segments, suggesting limitations due to identity-by-descent detection accuracy. Last, we compared our Bayes classifier that includes segment numbers with both ERSA and IBIS and found comparable recalls, with the Bayes classifier and ERSA slightly outperforming each other across different degrees. Overall, this study shows that identity-by-descent segment numbers can improve relatedness inference, but errors from current SNP array-based detection methods yield dampened signals in practice.
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Affiliation(s)
- Jesse Smith
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
| | - Ying Qiao
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
| | - Amy L Williams
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
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2
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An Accelerating Reduction Approach for Incomplete Decision Table Using Positive Approximation Set. SENSORS 2022; 22:s22062211. [PMID: 35336382 PMCID: PMC8949348 DOI: 10.3390/s22062211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 12/04/2022]
Abstract
Due to the explosive growth of data collected by various sensors, it has become a difficult problem determining how to conduct feature selection more efficiently. To address this problem, we offer a fresh insight into rough set theory from the perspective of a positive approximation set. It is found that a granularity domain can be used to characterize the target knowledge, because of its form of a covering with respect to a tolerance relation. On the basis of this fact, a novel heuristic approach ARIPA is proposed to accelerate representative reduction algorithms for incomplete decision table. As a result, ARIPA in classical rough set model and ARIPA-IVPR in variable precision rough set model are realized respectively. Moreover, ARIPA is adopted to improve the computational efficiency of two existing state-of-the-art reduction algorithms. To demonstrate the effectiveness of the improved algorithms, a variety of experiments utilizing four UCI incomplete data sets are conducted. The performances of improved algorithms are compared with those of original ones as well. Numerical experiments justify that our accelerating approach enhances the existing algorithms to accomplish the reduction task more quickly. In some cases, they fulfill attribute reduction even more stably than the original algorithms do.
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Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection. REMOTE SENSING 2021. [DOI: 10.3390/rs13204118] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial–spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial–spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information’s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial–spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data.
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Mera-Gaona M, Neumann U, Vargas-Canas R, López DM. Evaluating the impact of multivariate imputation by MICE in feature selection. PLoS One 2021; 16:e0254720. [PMID: 34320016 PMCID: PMC8318311 DOI: 10.1371/journal.pone.0254720] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 07/01/2021] [Indexed: 11/18/2022] Open
Abstract
Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets. AIM Demonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values. RESULTS We compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets. CONCLUSIONS Considering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.
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Affiliation(s)
| | - Ursula Neumann
- Data Science and Optimization, Fraunhofer Center for Applied Research on Supply Chain Services SCS, Nuremberg, Bayern, Germany
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Khan AJ, Raza B, Shahid AR, Kumar YJ, Faheem M, Alquhayz H. Handling incomplete data classification using imputed feature selected bagging (IFBag) method. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.
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Affiliation(s)
- Ahmad Jaffar Khan
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Basit Raza
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ahmad Raza Shahid
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Yogan Jaya Kumar
- Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
| | - Muhammad Faheem
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Hani Alquhayz
- Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, Saudi Arabia
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Chen C, Zhou L, Ji X, He G, Dai Y, Dang Y. Adaptive Modeling Strategy Integrating Feature Selection and Random Forest for Fluid Catalytic Cracking Processes. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01409] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Chen Chen
- Department of Chemical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - Li Zhou
- Department of Chemical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - Xu Ji
- Department of Chemical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - Ge He
- Department of Chemical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - Yiyang Dai
- Department of Chemical Engineering, Sichuan University, Chengdu 610065, P. R. China
| | - Yagu Dang
- Department of Chemical Engineering, Sichuan University, Chengdu 610065, P. R. China
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An Efficient Alert Aggregation Method Based on Conditional Rough Entropy and Knowledge Granularity. ENTROPY 2020; 22:e22030324. [PMID: 33286098 PMCID: PMC7516779 DOI: 10.3390/e22030324] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/08/2020] [Accepted: 03/10/2020] [Indexed: 11/17/2022]
Abstract
With the emergence of network security issues, various security devices that generate a large number of logs and alerts are widely used. This paper proposes an alert aggregation scheme that is based on conditional rough entropy and knowledge granularity to solve the problem of repetitive and redundant alert information in network security devices. Firstly, we use conditional rough entropy and knowledge granularity to determine the attribute weights. This method can determine the different important attributes and their weights for different types of attacks. We can calculate the similarity value of two alerts by weighting based on the results of attribute weighting. Subsequently, the sliding time window method is used to aggregate the alerts whose similarity value is larger than a threshold, which is set to reduce the redundant alerts. Finally, the proposed scheme is applied to the CIC-IDS 2018 dataset and the DARPA 98 dataset. The experimental results show that this method can effectively reduce the redundant alerts and improve the efficiency of data processing, thus providing accurate and concise data for the next stage of alert fusion and analysis.
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Shu W, Qian W, Xie Y, Tang Z. An Efficient Uncertainty Measure-based Attribute Reduction Approach for Interval-valued Data with Missing Values. INT J UNCERTAIN FUZZ 2019. [DOI: 10.1142/s0218488519500417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Attribute reduction plays an important role in knowledge discovery and data mining. Confronted with data characterized by the interval and missing values in many data analysis tasks, it is interesting to research the attribute reduction for interval-valued data with missing values. Uncertainty measures can supply efficient viewpoints, which help us to disclose the substantive characteristics of such data. Therefore, this paper addresses the attribute reduction problem based on uncertainty measure for interval-valued data with missing values. At first, an uncertainty measure is provided for measuring candidate attributes, and then an efficient attribute reduction algorithm is developed for the interval-valued data with missing values. To improve the efficiency of attribute reduction, the objects that fall within the positive region are deleted from the whole object set in the process of selecting attributes. Finally, experimental results demonstrate that the proposed algorithm can find a subset of attributes in much shorter time than existing attribute reduction algorithms without losing the classification performance.
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Affiliation(s)
- Wenhao Shu
- School of Information Engineering, East China Jiaotong University, Nanchang 330013, P.R. China
| | - Wenbin Qian
- School of Software, Jiangxi Agricultural University, Nanchang 330045, P.R. China
| | - Yonghong Xie
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, P.R. China
| | - Zhaoping Tang
- School of Information Engineering, East China Jiaotong University, Nanchang 330013, P.R. China
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11
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Sun L, Wang L, Qian Y, Xu J, Zhang S. Feature selection using Lebesgue and entropy measures for incomplete neighborhood decision systems. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.104942] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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12
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Sun L, Wang L, Qian Y, Xu J, Zhang S. Feature selection using Lebesgue and entropy measures for incomplete neighborhood decision systems. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.104942 10.1016/j.knosys.2019.104942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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13
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Mu T, Zhang X, Mo Z. Double-Granule Conditional-Entropies Based on Three-Level Granular Structures. ENTROPY (BASEL, SWITZERLAND) 2019; 21:e21070657. [PMID: 33267371 PMCID: PMC7515153 DOI: 10.3390/e21070657] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/26/2019] [Accepted: 06/27/2019] [Indexed: 06/12/2023]
Abstract
Rough set theory is an important approach for data mining, and it refers to Shannon's information measures for uncertainty measurements. The existing local conditional-entropies have both the second-order feature and application limitation. By improvements of hierarchical granulation, this paper establishes double-granule conditional-entropies based on three-level granular structures (i.e., micro-bottom, meso-middle, macro-top ), and then investigates the relevant properties. In terms of the decision table and its decision classification, double-granule conditional-entropies are proposed at micro-bottom by the dual condition-granule system. By virtue of successive granular summation integrations, they hierarchically evolve to meso-middle and macro-top, to respectively have part and complete condition-granulations. Then, the new measures acquire their number distribution, calculation algorithm, three bounds, and granulation non-monotonicity at three corresponding levels. Finally, the hierarchical constructions and achieved properties are effectively verified by decision table examples and data set experiments. Double-granule conditional-entropies carry the second-order characteristic and hierarchical granulation to deepen both the classical entropy system and local conditional-entropies, and thus they become novel uncertainty measures for information processing and knowledge reasoning.
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Affiliation(s)
- Taopin Mu
- School of Mathematical Sciences, Sichuan Normal University, Chengdu 610066, China
- Institute of Intelligent Information and Quantum Information, Sichuan Normal University, Chengdu 610066, China
| | - Xianyong Zhang
- School of Mathematical Sciences, Sichuan Normal University, Chengdu 610066, China
- Institute of Intelligent Information and Quantum Information, Sichuan Normal University, Chengdu 610066, China
| | - Zhiwen Mo
- School of Mathematical Sciences, Sichuan Normal University, Chengdu 610066, China
- Institute of Intelligent Information and Quantum Information, Sichuan Normal University, Chengdu 610066, China
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14
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15
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Arauzo-Azofra A, Jiménez-Vílchez A, Molina-Baena J, Luque-Rodriguez M. Algorithmic cache of sorted tables for feature selection. Data Min Knowl Discov 2019. [DOI: 10.1007/s10618-019-00620-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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16
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Deng X, Deng J. Incipient Fault Detection for Chemical Processes Using Two-Dimensional Weighted SLKPCA. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b04794] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xiaogang Deng
- College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
| | - Jiawei Deng
- College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
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17
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Qian W, Shu W. Attribute reduction in incomplete ordered information systems with fuzzy decision. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.08.032] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Tran CT, Zhang M, Andreae P, Xue B, Bui LT. Improving performance of classification on incomplete data using feature selection and clustering. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.09.026] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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19
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Uechi L, Galas DJ, Sakhanenko NA. Multivariate Analysis of Data Sets with Missing Values: An Information Theory-Based Reliability Function. J Comput Biol 2018; 26:152-171. [PMID: 30495984 PMCID: PMC6383577 DOI: 10.1089/cmb.2018.0179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Missing values in complex biological data sets have significant impacts on our ability to correctly detect and quantify interactions in biological systems and to infer relationships accurately. In this article, we propose a useful metaphor to show that information theory measures, such as mutual information and interaction information, can be employed directly for evaluating multivariable dependencies even if data contain some missing values. The metaphor is that of thinking of variable dependencies as information channels between and among variables. In this view, missing data can be thought of as noise that reduces the channel capacity in predictable ways. We extract the available information in the data even if there are missing values and use the notion of channel capacity to assess the reliability of the result. This avoids the common practice—in the absence of prior knowledge of random imputation—of eliminating samples entirely, thus losing the information they can provide. We show how this reliability function can be implemented for pairs of variables, and generalize it for an arbitrary number of variables. Illustrations of the reliability functions for several cases are provided using simulated data.
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Affiliation(s)
- Lisa Uechi
- Pacific Northwest Research Institute, Seattle, Washington
| | - David J. Galas
- Pacific Northwest Research Institute, Seattle, Washington
- Address correspondence to: Dr. David J. Galas, Pacific Northwest Research Institute, 720 Broadway, Seattle, WA 98119
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20
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A parallel rough set based dependency calculation method for efficient feature selection. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.10.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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21
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Tran CT, Zhang M, Andreae P, Xue B, Bui LT. An effective and efficient approach to classification with incomplete data. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.05.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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22
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Feature selection using rough set-based direct dependency calculation by avoiding the positive region. Int J Approx Reason 2018. [DOI: 10.1016/j.ijar.2017.10.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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23
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Dynamic computing rough approximations approach to time-evolving information granule interval-valued ordered information system. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.06.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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24
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Extreme learning machine based mutual information estimation with application to time-series change-points detection. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2015.11.138] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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25
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Hu D, Wu J, Tian K, Liao L, Xu M, Du Y. Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing. J Environ Sci (China) 2017; 59:30-38. [PMID: 28888236 DOI: 10.1016/j.jes.2017.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 12/06/2016] [Accepted: 01/24/2017] [Indexed: 06/07/2023]
Abstract
A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3, 2016. The mean daily AQI and PM2.5 were 240.44 and 203.6μg/m3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM2.5 concentration and weather conditions. Southern sites (DX, YDM and DS) experienced heavier pollution than northern ones (DL, CP and WL). Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM2.5. Mutual information values of Air quality-Traffic-Meteorology (ATM-MI) revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO2.
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Affiliation(s)
- Dongmei Hu
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
| | - Jianping Wu
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
| | - Kun Tian
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China
| | - Lyuchao Liao
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China; Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian 350108, China
| | - Ming Xu
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China
| | - Yiman Du
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China
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A hybrid time series prediction model based on recurrent neural network and double joint linear–nonlinear extreme learning network for prediction of carbon efficiency in iron ore sintering process. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.069] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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27
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Feng G, An B, Yang F, Wang H, Zhang L. Relevance popularity: A term event model based feature selection scheme for text classification. PLoS One 2017; 12:e0174341. [PMID: 28379986 PMCID: PMC5381872 DOI: 10.1371/journal.pone.0174341] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2016] [Accepted: 03/07/2017] [Indexed: 11/24/2022] Open
Abstract
Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.
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Affiliation(s)
- Guozhong Feng
- Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
- Key Laboratory for Applied Statistics of MOE, Northeast Normal University, Changchun, 130024, China
- Institute of Computational Biology, Northeast Normal University, Changchun, 130117, China
| | - Baiguo An
- School of Statistics, Capital University of Economics and Business, Beijing, 100070, China
| | - Fengqin Yang
- Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
| | - Han Wang
- Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
- Institute of Computational Biology, Northeast Normal University, Changchun, 130117, China
| | - Libiao Zhang
- Key Laboratory of Intelligent Information Processing of Jilin Universities, School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
- * E-mail:
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Xu W, Yu J. A novel approach to information fusion in multi-source datasets: A granular computing viewpoint. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.04.009] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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30
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Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems. ENERGIES 2016. [DOI: 10.3390/en10010007] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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31
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Improving performance for classification with incomplete data using wrapper-based feature selection. EVOLUTIONARY INTELLIGENCE 2016. [DOI: 10.1007/s12065-016-0141-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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