다중 레이블 인문 데이터의 효과적인 특징 선별을 위한 이주 개체 정제연산 기반 다중 개체군 유전알고리즘 = Effective Multi-population Genetic Algorithm using Migrant Refinement for Multi-label Humanity Data

다국어 초록 (Multilingual Abstract)

Multi-label feature selection is a preprocessing method that can be used to analyze, for example multi-label humanity data. In particular, a multi-population genetic algorithm is verified to exhibit a better performance for identifying an appropriate subset compared with existing genetic algorithms in that a variety of populations was preserved, and premature convergence was prevented. However, with this method, the inflow of closely related features to multi-labels is unlikely to search for the solution. This study proposes an effective multi-population genetic algorithm for multi-label feature selection. In the proposed method, a multi-population genetic algorithm with a refinement process in migrated individuals maintains a variety of populations, promotes the inflow of features closely related to multi-labels, and ultimately enhances the search performance. Experimental results indicate that the proposed method exhibit better performance than the compared multi-population algorithms.

참고문헌 (Reference)

1 Demšar, J., "Statistical comparisons of classifiers over multiple data sets" 7 : 1-30, 2006

2 Diplaris, S., "Protein classification with multiple algorithms" Springer 448-456, 2005

3 Ueda, N., "Parametric mixture models for multi-labeled text" 737-744, 2003

4 Dunn, O. J., "Multiple comparisons among means" 56 (56): 52-64, 1961

5 Ma, H., "Multi-population techniques in nature inspired optimization algorithms : A comprehensive survey" 44 : 365-387, 2019

6 Li, C., "Multi-population methods in unconstrained continuous dynamic environments : The challenges" 296 : 95-118, 2015

7 Lee, J., "Memetic feature selection algorithm for multi-label classification" 293 : 80-96, 2015

8 Boutell, M. R., "Learning multi-label scene classification" 37 (37): 1757-1771, 2004

9 Zawbaa, H. M., "Large-dimensionality small-instance set feature selection : a hybrid bio-inspired heuristic approach" 42 : 29-42, 2018

10 Li, F., "Granular multi-label feature selection based on mutual information" 67 : 410-423, 2017

11 Seo, W., "Generalized information-theoretic criterion for multi-label feature selection" 7 : 122854-122863, 2019

12 Cai, J., "Feature selection in machine learning : A new perspective" 300 : 70-79, 2018

13 Zhang, M. L., "Feature selection for multi-label naive Bayes classification" 179 (179): 3218-3229, 2009

14 Gu, S., "Feature selection for high-dimensional classification using a competitive swarm optimizer" 22 (22): 811-822, 2018

15 Wang, H., "Feature selection for classification of microarray gene expression cancers using Bacterial Colony Optimization with multi-dimensional population" 48 : 172-181, 2019

16 Gonzalez-Lopez, J., "Distributed multi-label feature selection using individual mutual information measures" 188 : 105052-, 2020

17 Zhang, P., "Distinguishing two types of labels for multi-label feature selection" 95 : 72-82, 2019

18 Pereira, R. B., "Correlation analysis of performance measures for multi-label classification" 54 (54): 359-369, 2018

19 Pereira, R. B., "Categorizing feature selection methods for multi-label classification" 49 (49): 57-78, 2018

20 Li, J. Y., "Artificial bee colony optimizer with bee-to-bee communication and multipopulation coevolution for multilevel threshold image segmentation" 2015

21 Madjarov, G., "An extensive experimental comparison of methods for multi-label learning" 45 (45): 3084-3104, 2012

22 Nseef, S. K., "An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems" 104 : 14-23, 2016

23 Ma, B., "A tribe competition-based genetic algorithm for feature selection in pattern classification" 58 : 328-338, 2017

24 Zhang, M. L., "A review on multi-label learning algorithms" 26 (26): 1819-1837, 2013

25 Qiu, C., "A novel multi-swarm particle swarm optimization for feature selection" 20 (20): 503-529, 2019

26 Zhang, W., "A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm : An application in credit scoring" 121 : 221-232, 2019

27 Kashef, S., "A label-specific multi-label feature selection algorithm based on the Pareto dominance concept" 88 : 654-667, 2019

28 Elisseeff, A., "A kernel method for multi-labelled classification" 14 : 681-687, 2001