期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 25, 期 6, 页码 1491-1507出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2017.2735947
关键词
Feature selection; fuzzy mutual information; label correlation; multilabel learning; streaming features
资金
- National Natural Science Foundation of China [61672272, 61303131, 61432011]
- China Postdoctoral Science Foundation [2015M581298]
- US NSF [IIS-1652107]
Due to complex semantics, a sample may be associated with multiple labels in various classification and recognition tasks. Multilabel learning generates trainingmodels tomap feature vectors to multiple labels. There are several significant challenges in multilabel learning. Samples in multilabel learning are usually described with high-dimensional features and some features may be sequentially extracted. Thus, we do not know the full feature set at the beginning of learning, referred to as streaming features. In this paper, we introduce fuzzy mutual information to evaluate the quality of features in multilabel learning, and design efficient algorithms to conduct multilabel feature selection when the feature space is completely known or partially known in advance. These algorithms are called multilabel feature selection with label correlation (MUCO) and multilabel streaming feature selection (MSFS), respectively. MSFS consists of two key steps: online relevance analysis and online redundancy analysis. In addition, we design a metric to measure the correlation between the label sets, and both MUCO and MSFS take label correlation to consideration. The proposed algorithms are not only able to select features from streaming features, but also able to select features for ordinal multilabel learning. However streaming feature selection is more efficient. The proposed algorithms are tested with a collection of multilabel learning tasks. The experimental results illustrate the effectiveness of the proposed algorithms.
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