Feature weighting by RELIEF based on local hyperplane approximation

Hongmin Cai*, Kwok Po NG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

In this paper, we propose a new feature weighting algorithm through the classical RELIEF framework. The key idea is to estimate the feature weights through local approximation rather than global measurement, as used in previous methods. The weights obtained by our method are more robust to degradation of noisy features, even when the number of dimensions is huge. To demonstrate the performance of our method, we conduct experiments on classification by combining hyperplane KNN model (HKNN) and the proposed feature weight scheme. Empirical study on both synthetic and real-world data sets demonstrate the superior performance of the feature selection for supervised learning, and the effectiveness of our algorithm.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 16th Pacific-Asia Conference, PAKDD 2012, Proceedings
Pages335-346
Number of pages12
EditionPART 2
DOIs
Publication statusPublished - 2012
Event16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012 - Kuala Lumpur, Malaysia
Duration: 29 May 20121 Jun 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7301 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012
Country/TerritoryMalaysia
CityKuala Lumpur
Period29/05/121/06/12

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Classification
  • Feature weighting
  • KNN
  • local hyperplane
  • RELIEF

Fingerprint

Dive into the research topics of 'Feature weighting by RELIEF based on local hyperplane approximation'. Together they form a unique fingerprint.

Cite this