scholarly journals A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information

2021 ◽  
Author(s):  
E Hancer ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
D Karaboga ◽  
B Akay
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Artificial Bee Colony ◽  
Feature Subset ◽  
Fitness Evaluation ◽  
Multi Objective ◽  
Bee Colony ◽  
Conflicting Objectives ◽  
Single Objective

© 2015 IEEE. Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective feature selection approaches are proposed by integrating MOABC with three filter fitness evaluation criteria, which are mutual information, fuzzy mutual information and the proposed fuzzy mutual information. The proposed multi-objective feature selection approaches are examined by comparing them with three single-objective ABC-based feature selection approaches on six commonly used datasets. The results show that the proposed approaches are able to achieve better performance than the original feature set in terms of the classification accuracy and the number of features. By using the same evaluation criterion, the proposed multi-objective algorithms generally perform better than the single-objective methods, especially in terms of reducing the number of features. Furthermore, the proposed fuzzy mutual information criterion outperforms mutual information and the original fuzzy mutual information in both single-objective and multi-objective manners. This work is the first study on multi-objective ABC for filter feature selection in classification, which shows that multi-objective ABC can be effectively used to address feature selection problems.

scholarly journals A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information

2021 ◽  
Author(s):  
E Hancer ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
D Karaboga ◽  
B Akay
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Artificial Bee Colony ◽  
Feature Subset ◽  
Fitness Evaluation ◽  
Multi Objective ◽  
Bee Colony ◽  
Conflicting Objectives ◽  
Single Objective

© 2015 IEEE. Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective feature selection approaches are proposed by integrating MOABC with three filter fitness evaluation criteria, which are mutual information, fuzzy mutual information and the proposed fuzzy mutual information. The proposed multi-objective feature selection approaches are examined by comparing them with three single-objective ABC-based feature selection approaches on six commonly used datasets. The results show that the proposed approaches are able to achieve better performance than the original feature set in terms of the classification accuracy and the number of features. By using the same evaluation criterion, the proposed multi-objective algorithms generally perform better than the single-objective methods, especially in terms of reducing the number of features. Furthermore, the proposed fuzzy mutual information criterion outperforms mutual information and the original fuzzy mutual information in both single-objective and multi-objective manners. This work is the first study on multi-objective ABC for filter feature selection in classification, which shows that multi-objective ABC can be effectively used to address feature selection problems.<div><br></div><div><div><table><tr><td>© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</td></tr></table></div></div>

scholarly journals Differential evolution for filter feature selection based on information theory and feature ranking

2021 ◽  
Author(s):  
E Hancer ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Differential Evolution ◽  
Nearest Neighbor ◽  
Feature Subset ◽  
Fisher Score ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Class Labels ◽  
Single Objective

© 2017 Elsevier B.V. Feature selection is an essential step in various tasks, where filter feature selection algorithms are increasingly attractive due to their simplicity and fast speed. A common filter is to use mutual information to estimate the relationships between each feature and the class labels (mutual relevancy), and between each pair of features (mutual redundancy). This strategy has gained popularity resulting a variety of criteria based on mutual information. Other well-known strategies are to order each feature based on the nearest neighbor distance as in ReliefF, and based on the between-class variance and the within-class variance as in Fisher Score. However, each strategy comes with its own advantages and disadvantages. This paper proposes a new filter criterion inspired by the concepts of mutual information, ReliefF and Fisher Score. Instead of using mutual redundancy, the proposed criterion tries to choose the highest ranked features determined by ReliefF and Fisher Score while providing the mutual relevance between features and the class labels. Based on the proposed criterion, two new differential evolution (DE) based filter approaches are developed. While the former uses the proposed criterion as a single objective problem in a weighted manner, the latter considers the proposed criterion in a multi-objective design. Moreover, a well known mutual information feature selection approach (MIFS) based on maximum-relevance and minimum-redundancy is also adopted in single-objective and multi-objective DE algorithms for feature selection. The results show that the proposed criterion outperforms MIFS in both single objective and multi-objective DE frameworks. The results also indicate that considering feature selection as a multi-objective problem can generally provide better performance in terms of the feature subset size and the classification accuracy. © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

scholarly journals Differential evolution for filter feature selection based on information theory and feature ranking

2021 ◽  
Author(s):  
E Hancer ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Differential Evolution ◽  
Nearest Neighbor ◽  
Feature Subset ◽  
Fisher Score ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Class Labels ◽  
Single Objective

© 2017 Elsevier B.V. Feature selection is an essential step in various tasks, where filter feature selection algorithms are increasingly attractive due to their simplicity and fast speed. A common filter is to use mutual information to estimate the relationships between each feature and the class labels (mutual relevancy), and between each pair of features (mutual redundancy). This strategy has gained popularity resulting a variety of criteria based on mutual information. Other well-known strategies are to order each feature based on the nearest neighbor distance as in ReliefF, and based on the between-class variance and the within-class variance as in Fisher Score. However, each strategy comes with its own advantages and disadvantages. This paper proposes a new filter criterion inspired by the concepts of mutual information, ReliefF and Fisher Score. Instead of using mutual redundancy, the proposed criterion tries to choose the highest ranked features determined by ReliefF and Fisher Score while providing the mutual relevance between features and the class labels. Based on the proposed criterion, two new differential evolution (DE) based filter approaches are developed. While the former uses the proposed criterion as a single objective problem in a weighted manner, the latter considers the proposed criterion in a multi-objective design. Moreover, a well known mutual information feature selection approach (MIFS) based on maximum-relevance and minimum-redundancy is also adopted in single-objective and multi-objective DE algorithms for feature selection. The results show that the proposed criterion outperforms MIFS in both single objective and multi-objective DE frameworks. The results also indicate that considering feature selection as a multi-objective problem can generally provide better performance in terms of the feature subset size and the classification accuracy. © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size

2020 ◽  
Vol 88 ◽  
pp. 106041 ◽  
Author(s):  
Xiao-han Wang ◽  
Yong Zhang ◽  
Xiao-yan Sun ◽  
Yong-li Wang ◽  
Chang-he Du
Keyword(s):  
Feature Selection ◽  
Sample Size ◽  
Artificial Bee Colony ◽  
Variable Sample Size ◽  
Multi Objective ◽  
Bee Colony

scholarly journals Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Gürcan Yavuz ◽  
Doğan Aydin
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Continuous Optimization ◽  
Subset Selection ◽  
Machine Intelligence ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Bee Colony ◽  
Angle Modulation

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets.

scholarly journals A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

2021 ◽  
Author(s):  
Jing Wang ◽  
Yuanzi Zhang ◽  
Minglin Hong ◽  
Haiyang He ◽  
Shiguo Huang
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Adaptive Method ◽  
High Dimensional ◽  
Feature Subset ◽  
Feature Selection Problem ◽  
Bee Colony ◽  
Dimensional Classification ◽  
Self Adaptive

Abstract Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of “curse of dimensionality” when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

scholarly journals FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

2016 ◽  
Vol 9 (2) ◽  
pp. 106
Author(s):  
Ratri Enggar Pawening ◽  
Tio Darmawan ◽  
Rizqa Raaiqa Bintana ◽  
Agus Zainal Arifin ◽  
Darlis Herumurti
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Classification Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Feature Transformation ◽  
Selection Methods ◽  
Class Label ◽  
Svm Algorithm ◽  
Heterogeneous Features

Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT) is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI) for classifying heterogeneous features. We use unsupervised feature transformation (UFT) methods and joint mutual information maximation (JMIM) methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM) algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

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