Earticle

초록

영어

In recent years, multi-label learning has been used to deal with data attributed to multiple labels simultaneously and has been increasingly applied to various applications. As many other machine learning tasks, multi-label learning also suffers from the curse of dimensionality; so extracting good features using multiple labels of the datasets becomes an important step prior to classification. In this paper, we study the problem of multi-label feature selection for classification and have proposed a method based on single label feature selection ReliefF, termed ML-ReliefF, to select discriminant features in order to boost multi-label classification accuracy. Compared to other multi-label feature selection methods that only consider the relationship between pairwise classes, the proposed method introduces the concept of label set to further consider the relationship among more than two labels, modifies the regulation of the nearest neighbors computation reflecting the influence between samples and multiple labels, and considers and adds the similarity between samples to reinforce the effect. With the classifier, ML-kNN, experiments on five different datasets show that the proposed method is effective in removing irrelevant or redundant features and the selected features are more discriminant for classification.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Single Label ReliefF Feature Selection
  2.2. Multi-label Difficulties
 3. Multi-label ReliefF Feature Selection
  3.1. Aspects of ML-ReliefF
  3.2. ML-ReliefF Algorithm
 4. Experiment
  4.1. Comparison of Feature Selection Methods
  4.2. Consideration of Parameters
 5. Conclusion and Future Work
 Acknowledgments
 References

키워드

저자

• Yaping Cai [ School of Computer Science and Technology, Nanjing Normal University, Nanjing210023. PR.China ]
• Ming Yang [ School of Computer Science and Technology, Nanjing Normal University, Nanjing210023. PR.China ]
• Ming Yang [ State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing210023. PR.China ]
• Hujun Yin [ School of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL,UK ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605