Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection

Liu, Yanbin1,2; Yan, Yan2; Chen, Ling2; Han, Yahong3; Yang, Yi2

2019

33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence

2159-5399

2374-3468

THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE

4408-4415

JAN 27-FEB 01, 2019

null,Honolulu,HI

2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE

In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an l(0)-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the state-of-the-art online learning methods in the online-batch setting.

第一 ; 通讯

英语

CPCI-S ; EI

NSFC[U1509206]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic

WOS:000485292604053

20203509101560

E-learning ; Crime ; Learning systems

Social Sciences:971

Web of Science

1.Southern Univ Sci & Technol, SUSTech UTS Joint Ctr CIS, Shenzhen, Peoples R China
2.Univ Technol Sydney, Ctr Artificial Intelligence, Sydney, NSW, Australia
3.Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China

Liu, Yanbin,Yan, Yan,Chen, Ling,et al. Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2019:4408-4415.