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Debata PP, Mohapatra P. Selection of informative genes from high-dimensional cancerous data employing an improvised meta-heuristic algorithm. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00593-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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2
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Bala I, Yadav A. Comprehensive learning gravitational search algorithm for global optimization of multimodal functions. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04250-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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3
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A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm. Symmetry (Basel) 2018. [DOI: 10.3390/sym10110609] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.
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Kushwaha N, Pant M, Kant S, Jain VK. Magnetic optimization algorithm for data clustering. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2017.10.031] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Ebrahimpour MK, Nezamabadi-pour H, Eftekhari M. CCFS: A cooperating coevolution technique for large scale feature selection on microarray datasets. Comput Biol Chem 2018; 73:171-178. [DOI: 10.1016/j.compbiolchem.2018.02.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 01/10/2018] [Accepted: 02/07/2018] [Indexed: 11/16/2022]
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6
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A band selection method for airborne hyperspectral image based on chaotic binary coded gravitational search algorithm. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.07.059] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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7
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Fuzzy decision function estimation using fuzzified particle swarm optimization. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-016-0561-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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8
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Priyadarshini R, Barik RK, Dash N, Mishra BK, Misra R. A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2017. [DOI: 10.4018/ijamc.2017100106] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Lots of research has been carried out globally to design a machine classifier which could predict it from some physical and bio-medical parameters. In this work a hybrid machine learning classifier has been proposed to design an artificial predictor to correctly classify diabetic and non-diabetic people. The classifier is an amalgamation of the widely used K-means algorithm and Gravitational search algorithm (GSA). GSA has been used as an optimization tool which will compute the best centroids from the two classes of training data; the positive class (who are diabetic) and negative class (who are non-diabetic). In K-means algorithm instead of using random samples as initial cluster head, the optimized centroids from GSA are used as the cluster centers. The inherent problem associated with k-means algorithm is the initial placement of cluster centers, which may cause convergence delay thereby degrading the overall performance. This problem is tried to overcome by using a combined GSA and K-means.
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Affiliation(s)
| | | | - Nilamadhab Dash
- Department of Information Technology. C.V. Raman College of Engineering, Bhubaneswar, India
| | - Brojo Kishore Mishra
- Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
| | - Rachita Misra
- Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
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Zandevakili H, Rashedi E, Mahani A. Gravitational search algorithm with both attractive and repulsive forces. Soft comput 2017. [DOI: 10.1007/s00500-017-2785-2] [Citation(s) in RCA: 19] [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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Barik RK, Priyadarshini R, Dash N. A Meta-Heuristic Model for Data Classification Using Target Optimization. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2017. [DOI: 10.4018/ijamc.2017070102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The paper contains an extensive experimental study which focuses on a major idea on Target Optimization (TO) prior to the training process of artificial machines. Generally, during training process of an artificial machine, output is computed from two important parameters i.e. input and target. In general practice input is taken from the training data and target is randomly chosen, which may not be relevant to the corresponding training data. Hence, the overall training of the neural network becomes inefficient. The present study tries to put forward TO as an efficient methodology which may be helpful in addressing the said problem. The proposed work tries to implement the concept of TO and compares the outcomes with the conventional classifiers. In this regard, different benchmark data sets are used to compare the effect of TO on data classification by using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) optimization techniques.
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Affiliation(s)
| | - Rojalina Priyadarshini
- Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
| | - Nilamadhab Dash
- Department of Information Technology, C. V. Raman College of Engineering, Bhubaneswar, India
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11
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Shahraki H, Zahiri SH. Ant colony optimization and decision function estimation. INTELLIGENT DECISION TECHNOLOGIES 2017. [DOI: 10.3233/idt-160278] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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12
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Li C, Mao Y, Zhou J, Zhang N, An X. Design of a fuzzy-PID controller for a nonlinear hydraulic turbine governing system by using a novel gravitational search algorithm based on Cauchy mutation and mass weighting. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.10.035] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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13
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Wan Y, Wang M, Ye Z, Lai X. A "Tuned" Mask Learnt Approach Based on Gravitational Search Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016; 2016:8179670. [PMID: 28090204 PMCID: PMC5206784 DOI: 10.1155/2016/8179670] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Revised: 11/09/2016] [Accepted: 11/15/2016] [Indexed: 11/17/2022]
Abstract
Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using "Tuned" mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, "Tuned" mask is viewed as a constrained optimization problem and the optimal "Tuned" mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal "Tuned" mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.
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Affiliation(s)
- Youchuan Wan
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
| | - Mingwei Wang
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
| | - Zhiwei Ye
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Xudong Lai
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
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Razavi SF, Sajedi H. Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015. [DOI: 10.3233/ifs-151700] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Seyedeh Fatemeh Razavi
- Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Hedieh Sajedi
- Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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Rezaei M, Nezamabadi-pour H. Using gravitational search algorithm in prototype generation for nearest neighbor classification. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.01.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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17
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Saha S, Kar R, Mandal D, Ghoshal S. Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2015. [DOI: 10.1016/j.jksuci.2014.03.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Yuan X, Ji B, Zhang S, Tian H, Hou Y. A new approach for unit commitment problem via binary gravitational search algorithm. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.05.029] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Sheikhan M. Generation of suprasegmental information for speech using a recurrent neural network and binary gravitational search algorithm for feature selection. APPL INTELL 2014. [DOI: 10.1007/s10489-013-0505-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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22
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Rashedi E, Nezamabadi-pour H, Saryazdi S. A simultaneous feature adaptation and feature selection method for content-based image retrieval systems. Knowl Based Syst 2013. [DOI: 10.1016/j.knosys.2012.10.011] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Sheikhan M, Jadidi Z. Flow-based anomaly detection in high-speed links using modified GSA-optimized neural network. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1263-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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25
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Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1204-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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