[1]JI Ruohan,DONG Hongbin.Feature selection using forest optimization algorithm based on duplication analysis[J].CAAI Transactions on Intelligent Systems,2022,17(6):1113-1122.[doi:10.11992/tis.202111060]
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Feature selection using forest optimization algorithm based on duplication analysis
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 6
Page number:
1113-1122
Column:
学术论文—机器学习
Public date:
2022-11-05
- Title:
- Feature selection using forest optimization algorithm based on duplication analysis
- Keywords:
- feature selection; evolutionary algorithm; duplication analysis; information entropy; information gain; restart mechanism; forest optimization algorithm; dimensionality reduction
- CLC:
- TP301
- DOI:
- 10.11992/tis.202111060
- Abstract:
- The forest optimization algorithm is an evolutionary algorithm based on the concept of forest tree planting. It has a strong capability for searching for feature space and low implementation difficulty. However, the algorithm still has room for improvement in the convergence speed and merit-seeking ability of the forest as a whole, and it is not well-suited to high dimensional data sets. In this paper, we propose to use a forest optimization algorithm based on duplication analysis (DAFSFOA) to address the above problems. The algorithm proposes an adaptive initialization strategy based on information gain, a forest repetition analysis mechanism, a forest restart mechanism, a candidate optimal tree generation strategy, and an adaptation function that integrates the number of feature selections and the correct classification rate. The experimental results show that DAFSFOA achieves the highest classification accuracy on most datasets. Meanwhile, for the high dimensional dataset SRBCT, DAFSFOA has a large improvement over feature selection using a forest optimization algorithm (FSFOA) in terms of dimensionality reduction rate and classification accuracy. DAFSFOA has a stronger feature space exploration capability than FSFOA and can adapt to datasets with different dimensions.
- References:
-
[1] LI Jundong, CHENG Kewei, WANG Suhang, et al. Feature selection: a data perspective[J]. ACM computing surveys, 2018, 50(6): 94.
[2] XUE Bing, ZHANG Mengjie, BROWNE W N, et al. A survey on evolutionary computation approaches to feature selection[J]. IEEE transactions on evolutionary computation, 2016, 20(4): 606–626.
[3] DASH M, LIU H. Feature selection for classification[J]. Intelligent data analysis, 1997, 1(1/2/3/4): 131–156.
[4] GHAEMI M, FEIZI-DERAKHSHI M R. Forest optimization algorithm[J]. Expert systems with applications, 2014, 41(15): 6676–6687.
[5] GHAEMI M, FEIZI-DERAKHSHI M R. Feature selection using Forest Optimization Algorithm[J]. Pattern recognition, 2016, 60: 121–129.
[6] 初蓓, 李占山, 张梦林, 等. 基于森林优化特征选择算法的改进研究[J]. 软件学报, 2018, 29(9): 2547–2558
CHU Bei, LI Zhanshan, ZHANG Menglin, et al. Research on improvements of feature selection using forest optimization algorithm[J]. Journal of software, 2018, 29(9): 2547–2558
[7] UNLER A, MURAT A, CHINNAM R B. mr2PSO: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification[J]. Information sciences, 2011, 181(20): 4625–4641.
[8] CERVANTE L, BING Xue, ZHANG Mengjie, et al. Binary particle swarm optimisation for feature selection: a filter based approach[C]//2012 IEEE Congress on Evolutionary Computation. Brisbane, 2012: IEEE, 2012: 1-8.
[9] KANNAN S S, RAMARAJ N. A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm[J]. Knowledge-based systems, 2010, 23(6): 580–585.
[10] SONG Xianfang, ZHANG Yong, GONG Dunwei, et al. Feature selection using bare-bones particle swarm optimization with mutual information[J]. Pattern recognition, 2021, 112: 107804.
[11] XU Hang, XUE Bing, ZHANG Mengjie. A duplication analysis-based evolutionary algorithm for biobjective feature selection[J]. IEEE transactions on evolutionary computation, 2021, 25(2): 205–218.
[12] JOHN LU Z Q. The elements of statistical learning: data mining, inference, and prediction[J]. Journal of the royal statistical society:series A (statistics in society), 2010, 173(3): 693–694.
[13] SUN Zehang, BEBIS G, MILLER R. Object detection using feature subset selection[J]. Pattern recognition, 2004, 37(11): 2165–2176.
[14] DAVIES S, RUSSELL S. NP-completeness of searches for smallest possible feature sets[C]//AAAI Symposium on Intelligent Relevance. Menlo Park: AAAI Press, 1994: 37-39.
[15] MARCANO-CEDEO A, QUINTANILLA-DOMíNGUEZ J, CORTINA-JANUCHS M G, et al. Feature sele-ction using Sequential Forward Selection and classify-cation applying Artificial Metaplasticity Neural Netwo-rk[C]// IECON 2010—36th Annual Conference on IEEE Industrial Electronics Society. Glendale: IEEE, 2010: 2845-2850.
[16] PENG Hongyi, JIANG Chunfu, FANG Xiang, et al. Variable selection for Fisher linear discriminant analysis using the modified sequential backward selection algorithm for the microarray data[J]. Applied mathematics and computation, 2014, 238: 132–140.
[17] I??K ?. Dominant point detection based on suboptimal feature selection methods[J]. Expert systems with applications, 2020, 161: 113741.
[18] SETIAWAN D, KUSUMA W A, WIGENA A H. Sequential forward floating selection with two selection criteria[C]//2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS). Bali: IEEE, 2017: 395-400.
[19] SINGH P, CHAUDHURY S, PANIGRAHI B K. Hybrid MPSO-CNN: multi-level particle swarm optimized hyperparameters of convolutional neural network[J]. Swarm and evolutionary computation, 2021, 63: 100863.
[20] ZHOU Yu, ZHANG Wenjun, KANG Junhao, et al. A problem-specific non-dominated sorting genetic algorithm for supervised feature selection[J]. Information sciences, 2021, 547: 841–859.
[21] LI Yongbo, SOLEIMANI H, ZOHAL M. An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives[J]. Journal of cleaner production, 2019, 227: 1161–1172.
[22] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in engineering software, 2016, 95: 51–67.
[23] DONG Hongbin, PAN Yuyao, SUN Jing. High dimensional feature selection method of dual gbest based on PSO[C]//2020 IEEE Congress on Evolutionary Computation. Glasgow: IEEE, 2020: 1-8.
[24] AGRAWAL R K, KAUR B, SHARMA S. Quantum based Whale Optimization Algorithm for wrapper feature selection[J]. Applied soft computing, 2020, 89: 106092.
[25] LI Anda, XUE Bing, ZHANG Mengjie. Improved binary particle swarm optimization for feature selection with new initialization and search space reduction strategies[J]. Applied soft computing, 2021, 106: 107302.
[26] XIE Qi, CHENG Gengguo, ZHANG Xiao, et al. Feature selection using improved forest optimization algorithm[J]. Information technology and control, 2020, 49(2): 289–301.
[27] MOUSTAKIDIS S P, THEOCHARIS J B. SVM-FuzCoC: a novel SVM-based feature selection method using a fuzzy complementary criterion[J]. Pattern recognition, 2010, 43(11): 3712–3729.
[28] HU Qinghua, CHE Xunjian, ZHANG Lei, et al. Feature evaluation and selection based on neighborhood soft margin[J]. Neurocomputing, 2010, 73(10/11/12): 2114–2124.
[29] HUANG Jinjie, CAI Yunze, XU Xiaoming. A hybrid genetic algorithm for feature selection wrapper based on mutual information[J]. Pattern recognition letters, 2007, 28(13): 1825–1844.
[30] ZHU Wenzhi, SI Gangquan, ZHANG Yanbin, et al. Neighborhood effective information ratio for hybrid feature subset evaluation and selection[J]. Neurocomputing, 2013, 99: 25–37.
[31] TABAKHI S, MORADI P, AKHLAGHIAN F. An unsupervised feature selection algorithm based on ant colony optimization[J]. Engineering applications of artificial intelligence, 2014, 32: 112–123.
[32] XUE Bing, ZHANG Mengjie, BROWNE W N. Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms[J]. Applied soft computing, 2014, 18: 261–276.
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1900-01-01