Multi-objective evolutionary design of granular rule-based classifiers

A Granular Approach to Interval Output Estimation for Rule-Based Fuzzy Models

Rule-based fuzzy models play a dominant role in fuzzy modeling and come with extensive applications in the system modeling area. Due to the presence of system modeling error, it is impossible to construct a model that fits exactly the experimental evidence and, at the same time, exhibits high generalization capabilities. To alleviate these problems, in this study, we elaborate on a realization of granular outputs for rule-based fuzzy models with the aim of effectively quantifying the associated modeling errors. Through analyzing the characteristics of modeling errors, an error model is constructed to characterize deviations among the estimated outputs and the expected ones. The resulting granular model comes into play as an aggregation of the regression model and the error model. Information granularity plays a central role in the construction of granular outputs (intervals). The quality of the produced interval estimates is quantified in terms of the coverage and specificity criteria. The optimal allocation of information granularity is determined through a combined index involving these two criteria pertinent to the evaluation of interval outputs. A series of experimental studies is provided to demonstrate the effectiveness of the proposed approach and show its superiority over the traditional statistical-based method.

A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills

The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with single-modal schemes. Furthermore, the K -means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning.

A Decision Support Model for Hotel Recommendation Based on the Online Consumer Reviews Using Logarithmic Spherical Hesitant Fuzzy Information

Spherical hesitant fuzzy sets have recently become more popular in various fields. It was proposed as a generalization of picture hesitant fuzzy sets and Pythagorean hesitant fuzzy sets in order to deal with uncertainty and fuzziness information. Technique of Aggregation is one of the beneficial tools to aggregate the information. It has many crucial application areas such as decision-making, data mining, medical diagnosis, and pattern recognition. Keeping in view the importance of logarithmic function and aggregation operators, we proposed a novel algorithm to tackle the multi-attribute decision-making (MADM) problems. First, novel logarithmic operational laws are developed based on the logarithmic, t-norm, and t-conorm functions. Using these operational laws, we developed a list of logarithmic spherical hesitant fuzzy weighted averaging/geometric aggregation operators to aggregate the spherical hesitant fuzzy information. Furthermore, we developed the spherical hesitant fuzzy entropy to determine the unknown attribute weight information. Finally, the design principles for the spherical hesitant fuzzy decision-making have been developed, and a practical case study of hotel recommendation based on the online consumer reviews has been taken to illustrate the validity and superiority of presented approach. Besides this, a validity test is conducted to reveal the advantages and effectiveness of developed approach. Results indicate that the proposed method is suitable and effective for the decision process to evaluate their best alternative.

Webpage Recommendation System Using Interesting Subgraphs and Laplace Based k-Nearest Neighbor

An interesting research area that permits the user to mine the significant information, called frequent subgraph, is Graph-Based Data Mining (GBDM). One of the well-known algorithms developed to extract frequent patterns is GASTON algorithm. Retrieving the interesting webpages from the log files contributes heavily to various applications. In this work, a webpage recommendation system has been proposed by introducing Chronological Cuckoo Search (Chronological-CS) algorithm and the Laplace correction based k-Nearest Neighbor (LKNN) to retrieve the useful webpage from the interesting webpage. Initially, W-Gaston algorithm extracts the interesting subgraph from the log files and provides it to the proposed webpage recommendation system. The interesting subgraphs subjected to clustering with the proposed Chronological-CS algorithm, which is developed by integrating the chronological concept into Cuckoo Search (CS) algorithm, provide various cluster groups. Then, the proposed LKNN algorithm recommends the webpage from the clusters. Simulation of the proposed webpage recommendation algorithm is done by utilizing the data from MSNBC and weblog database. The results are compared with various existing webpage recommendation models and analyzed based on precision, recall, and F-measure. The proposed webpage recommendation model achieved better performance than the existing models with the values of 0.9194, 0.8947, and 0.86736, respectively, for the precision, recall, and F-measure.

Active Learning Using Fuzzy-Rough Nearest Neighbor Classifier for Cancer Prediction from Microarray Gene Expression Data

Cancer prediction from gene expression data is a very challenging area of research in the field of computational biology and bioinformatics. Conventional classifiers are often unable to achieve desired accuracy due to the lack of ‘sufficient’ training patterns in terms of clinically labeled samples. Active learning technique, in this respect, can be useful as it automatically finds only few most informative (or confusing) samples to get their class labels from the experts and those are added to the training set, which can improve the accuracy of the prediction consequently. A novel active learning technique using fuzzy-rough nearest neighbor classifier (ALFRNN) is proposed in this paper for cancer classification from microarray gene expression data. The proposed ALFRNN method is capable of dealing with the uncertainty, overlapping and indiscernibility often present in cancer subtypes (classes) of the gene expression data. The performance of the proposed method is tested using different real-life microarray gene expression cancer datasets and its performance is compared with five other state-of-the-art techniques (out of which three are active learning-based and two are traditional classification methods) in terms of percentage accuracy, precision, recall, [Formula: see text]-measures and kappa. Superiority of the proposed method over the other counterpart algorithms is established from experimental results for cancer prediction and results of the paired [Formula: see text]-test confirm statistical significance of the results in favor of the proposed method for almost all the datasets.

WHO: A New Evolutionary Algorithm Bio-Inspired by Wildebeests with a Case Study on Bank Customer Segmentation

Numerous evolutionary algorithms have been proposed which are inspired by the amazing lives of creatures, such as animals, insects, and birds. Each inspired algorithm has its own advantages and disadvantages, and has its own way to accomplish exploration and exploitation. In this paper, a new evolutionary algorithm with novel concepts, called Wildebeests Herd Optimization (WHO), is proposed. This algorithm is inspired by the splendid life of wildebeests in Africa. Moving and migration are inseparable from wildebeests’ lives. When a wildebeest wants to choose its path during migration, it considers the best path known to itself, the location of the more mature wildebeests in the crowd, and the direction of wildebeests with high mobility. The WHO algorithm imitates these traits, and can concurrently explore and exploit the search space. For validating WHO, it is applied to optimization problems and data mining tasks. It is demonstrated that WHO outperforms other evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization, in the assessed problems. Then, WHO is applied to the customer segmentation problem. Customer segmentation is one of the most important tasks of data mining, especially in the banking sector. In this paper, the customers of a bank with current accounts are segmented using WHO based on four aspects: profitability, cost, loyalty and credit; some of these aspects are calculated in a novel way. The results were welcome by the bank authorities.

An Improved Robust Fuzzy Algorithm for Unsupervised Learning

This paper presents a robust, dynamic, and unsupervised fuzzy learning algorithm (RDUFL) that aims to cluster a set of data samples with the ability to detect outliers and assign the numbers of clusters automatically. It consists of three main stages. The first (1) stage is a pre-processing method in which possible outliers are determined and quarantined using a concept of proximity degree. The second (2) stage is a learning method, which consists in auto-detecting the number of classes with their prototypes for a dynamic threshold. This threshold is automatically determined based on the similarity among the detected prototypes that are updated at the exploration of a new data. The last (3) stage treats quarantined samples detected from the first stage to determine whether they belong to some class defined in the second phase. The effectiveness of this method is assessed on eight real medical benchmark datasets in comparison to known unsupervised learning methods, namely, the fuzzy c-means (FCM), possibilistic c-means (PCM), and noise clustering (NC). The obtained accuracy of our scheme is very promising for unsupervised learning problems.

A Modified Intuitionistic Fuzzy Clustering Algorithm for Medical Image Segmentation

This paper presents a modified intuitionistic fuzzy clustering (IFCM) algorithm for medical image segmentation. IFCM is a variant of the conventional fuzzy C-means (FCM) based on intuitionistic fuzzy set (IFS) theory. Unlike FCM, IFCM considers both membership and nonmembership values. The existing IFCM method uses Sugeno’s and Yager’s IFS generators to compute nonmembership value. But for certain parameters, IFS constructed using above complement generators does not satisfy the elementary condition of intuitionism. To overcome this problem, this paper adopts a new IFS generator. Further, Hausdorff distance is used as distance metric to calculate the distance between cluster center and pixel. Extensive experimentations are carried out on standard datasets like brain, lungs, liver and breast images. This paper compares the proposed method with other IFS based methods. The proposed algorithm satisfies the elementary condition of intuitionism. Further, this algorithm outperforms other methods with the use of various cluster validity functions.

Adaptive Granulation-Based Prediction for Energy System of Steel Industry

The flow variation tendency of byproduct gas plays a crucial role for energy scheduling in steel industry. An accurate prediction of its future trends will be significantly beneficial for the economic profits of steel enterprise. In this paper, a long-term prediction model for the energy system is proposed by providing an adaptive granulation-based method that considers the production semantics involved in the fluctuation tendency of the energy data, and partitions them into a series of information granules. To fully reflect the corresponding data characteristics of the formed unequal-length temporal granules, a 3-D feature space consisting of the timespan, the amplitude and the linetype is designed as linguistic descriptors. In particular, a collaborative-conditional fuzzy clustering method is proposed to granularize the tendency-based feature descriptors and specifically measure the amplitude variation of industrial data which plays a dominant role in the feature space. To quantify the performance of the proposed method, a series of real-world industrial data coming from the energy data center of a steel plant is employed to conduct the comparative experiments. The experimental results demonstrate that the proposed method successively satisfies the requirements of the practically viable prediction.

A Prototype Intelligent Decision-Support System with a Unified Planning and Learning Capabilities

This article summarizes reviews regarding the evolution of intelligent decision-support systems (IDSS). After doing an extensive literature survey, it was apparent that the theoretical foundation of IDSS has not undergone much improvement. The emergence and development of Artificial General Intelligence (AGI) provides a new theoretical perspective for constructing and developing IDSS. An AGI project, Non-Axiomatic Reasoning System (NARS), is built into the framework of a unified reasoning system, with a logic, Non-Axiomatic Logic (NAL). In this paper, we propose a formal model with unified planning, learning and uncertainty representation capabilities using NAL. We have developed a prototype decision-support system for urban fire-fighting within this framework, and obtained results demonstrating that this method can provide an effective way for intelligent decision-support systems.

Limited Rationality and Its Quantification Through the Interval Number Judgments With Permutations

The relative importance of alternatives expressed in terms of interval numbers in the fuzzy analytic hierarchy process aims to capture the uncertainty experienced by decision makers (DMs) when making a series of comparisons. Under the assumption of full rationality, the judgements of DMs in the typical analytic hierarchy process could be consistent. However, since the uncertainty in articulating the opinions of DMs is unavoidable, the interval number judgements are associated with the limited rationality. In this paper, we investigate the concept of limited rationality by introducing interval multiplicative reciprocal comparison matrices. By analyzing the consistency of interval multiplicative reciprocal comparison matrices, it is observed that the interval number judgements are inconsistent. By considering the permutations of alternatives, the concepts of approximation-consistency and acceptable approximation-consistency of interval multiplicative reciprocal comparison matrices are proposed. The exchange method is designed to generate all the permutations. A novel method of determining the interval weight vector is proposed under the consideration of randomness in comparing alternatives, and a vector of interval weights is determined. A new algorithm of solving decision making problems with interval multiplicative reciprocal preference relations is provided. Two numerical examples are carried out to illustrate the proposed approach and offer a comparison with the methods available in the literature.

An Improved Centroid-Based Boundary Constraint-Handling Method in Differential Evolution for Constrained Optimization

Differential Evolution (DE) is a population-based Evolutionary Algorithm (EA) for solving optimization problems over continuous spaces. Many optimization problems are constrained and have a bounded search space from which some vectors leave when the mutation operator of DE is applied. Therefore, it is necessary the use of a boundary constraint-handling method (BCHM) in order to repair the invalid mutant vectors. This paper presents a generalized and improved version of the Centroid BCHM in order to keep the search within the valid ranges of decision variables in constrained numerical optimization problems (CNOPs), which has been tested on a robust and comprehensive set of experiments that include a variant of DE specialized in dealing with CNOPs. This new version, named Centroid [Formula: see text], relocates the mutant vector in the centroid formed by K random vectors and one vector taken from the population that is within or near the feasible region. The results show that this new version has a major impact on the algorithm’s performance, and it is able to promote better final results through the improvement of both, the approach to the feasible region and the ability to generate better solutions.