A multilabel feature selection algorithm by exploiting label correlations locally
Cai Yaping，Yang Ming*
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College of Computer Science，Nanjing Normal University，Nanjing，210023，China
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Published
2016-07-24
Issue Date
2016-07-24
Abstract
Different from traditional supervised learning framework in which each object is assigned to only one concept of label，the condition which one object may be associated with multiple labels simultaneously in multilabel learning is able to analyze the problems in the real world more effectively.In recent years，multilabel learning has been attracting a great deal of attention in machine learning.In multilabel learning，each object may be associated with multiple labels simultaneously，and these labels are related to each other.So how to effectively discover and exploit correlations among labels is the core research issue of multilabel learning.A series of multilabel learning algorithms by exploiting label correlations have been proposed and applied successfully in many application areas.However，there are a lot of redundant features and irrelevant features existing in high dimensional data which reduce the performance of classifiers，and few multilabel feature selection algorithms consider the label correlations.Meanwhile，most of the existed algorithms for multilabel feature selection exploit the global label correlations，which assuming that the label correlations are shared by all the instances.However，in realworld tasks，different instances may share different label correlations.With this respect，we focuse on how to exploit the label correlations locally to improve multilabel feature selection and help multilabel classification.In this paper，a novel multilabel feature selection algorithm by exploiting the label correlation locally(LocMLFS)is introduced.The algorithm takes advantages of local label correlations(the correlations are not shared by all instances)in multilabel feature selection algorithm.To achieve the use of local label correlations，LocMLFS divides the samples into groups by category clustering and use multilabel feature selection to each group.At the same time，the algorithm can be extended to a unified framework.Experimental results on the datasets demonstrate that LocMLFS achieves superior performance.
Cai Yaping，Yang Ming* .
A multilabel feature selection algorithm by exploiting label correlations locally[J]. Journal of Nanjing University(Natural Sciences), 2016, 52(4): 693
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