Unsupervised probabilistic feature selection using ant colony optimization |
| |
| Affiliation: | 1. College of Mathematics Physics and Information Engineering, Jiaxing University, 56 Yuexiu Road (South), Jiaxing 314001, China;2. School of Computer Science and Information Technology, RMIT University, GPO Box 2476, Melbourne 3001 Victoria, Australia;3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;1. Shanghai University of Finance and Economics, Shanghai 200433, China;2. Institut-Mines Télécom, Télécom SudParis, 9 rue Charles Fourier, Evry Cedex 91011, France;3. Universidad Carlos III de Madrid, Av de la Universidad, 30, Leganés 28911, Madrid, Spain;4. School of Earth Science and Engineering, Hohai University, Nanjing 210098, China;5. State Key Laboratory of Geo-information Engineering, Xi’an 710054, China;1. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China;2. School of Mathematics and Computer Science, Northwest University for Nationalities, Lanzhou 730030, China;1. Faculty of Engineering and Technology, Birzeit University, Birzeit, Palestine;2. Department of Computer Science, Birzeit University, Birzeit, Palestine;3. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran;4. Department of Computer Science, School of Computing, National University of Singapore, Singapore;5. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan;6. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD 4111, Australia;7. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam |
| |
| Abstract: | Feature selection (FS) is one of the most important fields in pattern recognition, which aims to pick a subset of relevant and informative features from an original feature set. There are two kinds of FS algorithms depending on the presence of information about dataset class labels: supervised and unsupervised algorithms. Supervised approaches utilize class labels of dataset in the process of feature selection. On the other hand, unsupervised algorithms act in the absence of class labels, which makes their process more difficult. In this paper, we propose unsupervised probabilistic feature selection using ant colony optimization (UPFS). The algorithm looks for the optimal feature subset in an iterative process. In this algorithm, we utilize inter-feature information which shows the similarity between the features that leads the algorithm to decreased redundancy in the final set. In each step of the ACO algorithm, to select the next potential feature, we calculate the amount of redundancy between current feature and all those which have been selected thus far. In addition, we utilize a matrix to hold ant related pheromone which shows the rate of the co-presence of every pair of features in solutions. Afterwards, features are ranked based on a probability function extracted from the matrix; then, their m-top is returned as the final solution. We compare the performance of UPFS with 15 well-known supervised and unsupervised feature selection methods using different classifiers (support vector machine, naive Bayes, and k-nearest neighbor) on 10 well-known datasets. The experimental results show the efficiency of the proposed method compared to the previous related methods. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
