[1]GUO Yumeng,LI Guozheng.A filtering framework for the multi-label feature selection[J].CAAI Transactions on Intelligent Systems,2014,9(3):292-297.[doi:10.3969/j.issn.1673-4785.201403064]
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A filtering framework for the multi-label feature selection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 3 Page number: 292-297 Column: 学术论文—人工智能基础 Public date: 2014-06-25
Title:
A filtering framework for the multi-label feature selection
Author(s):
GUO Yumeng;  LI Guozheng
School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Keywords:
feature selection; multi-label; filter; CHI-square test
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201403064
Abstract:
The researchers of multi-label learning mainly focus on the classifier performance, regardless of the influence of the dataset feature. This paper proposes a filter framework of the multi-labeled data feature selection. The algorithm implementation and experiment were carried out based on the Chi-square test. This framework calculates the CHI-square test for each feature on each label, and then the ranking order of each feature is computed by the statistics of the score. This paper considers three different types of statistical data (average, maximum, minimum) for the experimental comparisons. The contrasting experiments with the four common multi-label datasets with three classifiers and five evaluation criteria show that these three score statistical methods share both superior and inferior characteristics, but still improve the performance for multi-label learning problems.
References:
[1] TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Mining Multi-label Data[R]. Data Minging and Knowledge Discovery Handbook, 2010:667-685.
[2] TSOUMAKAS G, KATAKIS I. Multi-label classification:an overview[J]. International Journal of Data Wareh -ousing and Mining, 2007, 40(3):1-13.
[3] ZHANG M L, ZHANG K. Multi-label learning by exploiting label dependency[C]//Proceedings of the 16th ACM SIG-KDD International Conference on Knowledge Discovery and Data Mining. Washington, DC, USA, 2010:999-1008.
[4] YANG Y, PEDERSEN J O. A comparative study on feature selection in text categorization[C]//Machine Learning International Workshop then Conference. Philadelphia, USA, 1997:412-420.
[5] SWATI S, GHATOL A, ASHOK C. Feature selection for medical diagnosis:Evaluation for cardiovascular diseases[J]. Expert Systems with Applications, 2013, 40(10):4146-4153.
[6] NEWTON S, EVERTON A C, MARIA C M, et al. A comparison of multi -label feature selection methods using the problem transformation approach[J]. Electronic Notes in Theoretical Computer Science, 2013, 292:135-151.
[7] 计智伟, 胡珉, 尹建新.特征选择算法综述[J].电子设计工程, 2011, 19(9):46-51.JI Zhiwei, HU Ming, YIN Jianxin. A survey of feature selection algorithm[J]. Electronic Design Engineering, 2011, 19(9):46-51.
[8] 邱云飞, 王威, 刘大有, 等.基于方差CHI的特征选择方法[J].计算机应用研究, 2012, 29(4):1301-1303.QIU Yunfei, WANG Wei, LIU Dayou, et al. CHI feature selection method based on variance[J]. Application Research of Computers, 2012, 29(4):1301-1303.
[9] ZHANG M L, ZHOU Z H. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 39(10):1-43.
[10] MATTHEW R B, LUO J B, SHEN X P, et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004, 37(9):1757-1771.
[11] READ J, PFAHRINGER B, HOLMES G, et al. Classifier chains for multi-label classification[J].Machine Learning, 2011, 85(3):333-359.
[12] ZHANG M L, ZHOU Z H. ML-kNN:a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7):2038-2048.
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