Adaptive Technique for Feature Selection in Modified Graph Clustering-Based Ant Colony Optimization

Modified graph clustering ant colony optimization (MGCACO) algorithm is an unsupervised feature selection (UFS) algorithm used in determining a subset of effective genes from microarray data. The feature subset construction is based on the ant colony optimization (ACO) algorithm, which guides the search process from clusters. However, the MGCACO algorithm is unable to choose all significant features from the clusters to form an optimal feature subset. This paper proposes an enhanced graph clustering ACO (EGCACO) to overcome the problem of feature selection in the MGCACO algorithm. A principal point of this algorithm is utilizing an adaptive selection technique that guides ACO for subset construction from the clusters of features. The adaptive technique for ant selection is based on the state of the search space. Experimental results indicated that the proposed EGCACO achieves the highest classification accuracy than five other common UFS algorithms on four classifiers, where it obtained 87.13%, 86 .19 %, 87.38 % and 90.80 % for support vector machine, k-nearest neighbor, decision tree and random forest classifiers, respectively. In particular, the proposed algorithm can select the genes of the deoxyribonucleic acid microarray with consideration of relevance and redundancy among the genes. Therefore, the proposed EGCACO can be implemented to handle the high dimension feature space, such as image processing, text classification, and microarray data processing, which is critical for good and reliable results.

[1]  Byung Ro Moon,et al.  Hybrid Genetic Algorithms for Feature Selection , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Ku Ruhana Ku-Mahamud,et al.  Hybrid Ant Colony Optimization and Genetic Algorithm for Rule Induction , 2020 .

[3]  Parham Moradi,et al.  Integration of graph clustering with ant colony optimization for feature selection , 2015, Knowl. Based Syst..

[4]  Ku Ruhana Ku-Mahamud,et al.  Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter , 2017, Int. J. Bio Inspired Comput..

[5]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[6]  Parham Moradi,et al.  An unsupervised feature selection algorithm based on ant colony optimization , 2014, Eng. Appl. Artif. Intell..

[7]  Juliana Wahid,et al.  Classification of Cervical Cancer Using Ant-Miner for Medical Expertise Knowledge Management , 2018 .

[8]  Ku Ruhana Ku-Mahamud,et al.  Grey Wolf Optimization Parameter Control for Feature Selection in Anomaly Detection , 2021 .

[9]  Günther Palm,et al.  Value-Difference Based Exploration: Adaptive Control between Epsilon-Greedy and Softmax , 2011, KI.

[10]  Belén Melián-Batista,et al.  Solving feature subset selection problem by a Parallel Scatter Search , 2006, Eur. J. Oper. Res..

[11]  Hojat Ghimatgar,et al.  An improved feature selection algorithm based on graph clustering and ant colony optimization , 2018, Knowl. Based Syst..

[12]  Diwakar Tripathi,et al.  A study on metaheuristics approaches for gene selection in microarray data: algorithms, applications and open challenges , 2019, Evolutionary Intelligence.

[13]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[14]  Ali Zakerolhosseini,et al.  Unsupervised probabilistic feature selection using ant colony optimization , 2016, Expert Syst. Appl..

[15]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Ku Ruhana Ku-Mahamud,et al.  Ant-based sorting and ACO-based clustering approaches: A review , 2018, 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).

[18]  Parham Moradi,et al.  Relevance-redundancy feature selection based on ant colony optimization , 2015, Pattern Recognit..

[19]  Li-Yeh Chuang,et al.  Improved binary particle swarm optimization using catfish effect for feature selection , 2011, Expert Syst. Appl..

[20]  Nadia Essoussi,et al.  Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient , 2018, Machine Learning Paradigms.

[21]  Lei Shi,et al.  Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search , 2018, Comput. Intell. Neurosci..

[22]  Ku Ruhana Ku-Mahamud,et al.  Annealing strategy for an enhance rule pruning technique in ACO-Based rule classification , 2019 .

[23]  Hayder Naser Khraibet,et al.  HYBRID ANT COLONY OPTIMIZATION AND ITERATED LOCAL SEARCH FOR RULES-BASED CLASSIFICATION , 2020 .

[24]  Steve R. Gunn,et al.  Result Analysis of the NIPS 2003 Feature Selection Challenge , 2004, NIPS.

[25]  Ku Ruhana Ku-Mahamud,et al.  Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm , 2020 .

[26]  Huan Liu,et al.  Spectral feature selection for supervised and unsupervised learning , 2007, ICML '07.

[27]  Chiman Salavati,et al.  Fast unsupervised feature selection based on the improved binary ant system and mutation strategy , 2019, Neural Computing and Applications.

[28]  Trevor J. Bihl,et al.  Feature Selection for RF Fingerprinting With Multiple Discriminant Analysis and Using ZigBee Device Emissions , 2016, IEEE Transactions on Information Forensics and Security.

[29]  Zhiwei Ye,et al.  A feature selection method based on modified binary coded ant colony optimization algorithm , 2016, Appl. Soft Comput..

[30]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[31]  Ku Ruhana Ku-Mahamud,et al.  An improved ACS algorithm for data clustering , 2020 .

[32]  José Fco. Martínez-Trinidad,et al.  A review of unsupervised feature selection methods , 2019, Artificial Intelligence Review.

[33]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[34]  Selma Ayse Özel,et al.  A hybrid approach of differential evolution and artificial bee colony for feature selection , 2016, Expert Syst. Appl..

[35]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[36]  Ali Ridho Barakbah,et al.  Ant colony algorithm for feature selection on microarray datasets , 2016, 2016 International Electronics Symposium (IES).

[37]  Behrouz Minaei-Bidgoli,et al.  Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism , 2018, Expert Syst. Appl..

[38]  Parham Moradi,et al.  Gene selection for microarray data classification using a novel ant colony optimization , 2015, Neurocomputing.

[39]  Shulin Wang,et al.  Feature selection in machine learning: A new perspective , 2018, Neurocomputing.

[40]  Jun Yan,et al.  Research Review on Algorithms of Community Detection in Complex Networks , 2018, Journal of Physics: Conference Series.

[41]  H WittenIan,et al.  The WEKA data mining software , 2009 .

[42]  Deng Cai,et al.  Unsupervised feature selection for multi-cluster data , 2010, KDD.

[43]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[44]  Zi Huang,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence ℓ2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning , 2022 .

[45]  Jiawei Han,et al.  Generalized Fisher Score for Feature Selection , 2011, UAI.

[46]  Li-Yeh Chuang,et al.  Chaotic maps based on binary particle swarm optimization for feature selection , 2011, Appl. Soft Comput..

[47]  Yi Jiang,et al.  Eigenvalue Sensitive Feature Selection , 2011, ICML.

[48]  Ku Ruhana Ku-Mahamud,et al.  Modified ACS Centroid Memory for Data Clustering , 2019, Journal of Computer Science.

[49]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[50]  Verónica Bolón-Canedo,et al.  A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..

[51]  Chiman Salavati,et al.  Hybrid fast unsupervised feature selection for high-dimensional data , 2019, Expert Syst. Appl..

[52]  Chidchanok Lursinsap,et al.  A Discrimination Analysis for Unsupervised Feature Selection via Optic Diffraction Principle , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[53]  Hugues Bersini,et al.  A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.