Instance-ranking: A new perspective to consider the instance dependency for classification

Xin Xia, Xiaohu Yang, Shanping Li, Chao Wu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


Single-label classification refers to the task to predict an instance to be one unique label in a set of labels. Different from single-label classification, for multi-label classification, one instance is associated with one or more labels in a set of labels simultaneously. Various works have focused on the algorithms for those two types of classification. Since the ranking problem is always coexisting with the classification problem, and traditional researches mainly assume the uniform distribution for the instances, in this paper, we propose a new perspective for the ranking problem. With the assumption that the distribution for the instance is not uniform, different instances have different influences for the distribution, the Instance-Ranking algorithm is presented. With the Instance- Ranking algorithm, the famous K-nearest-neighbors (KNN) algorithm is modified to confirm the validity of our algorithm. Lastly, the Instance-Ranking algorithm is combined with the ML.KNN algorithm for multi-label classification. Experiment with different datasets show that our Instance-Ranking algorithm achieves better performance than the original state-of-art algorithm such as KNN and ML.KNN.

Original languageEnglish
Title of host publicationEmerging Trends in Knowledge Discovery and Data Mining - PAKDD 2012 International Workshops
Subtitle of host publicationDMHM, GeoDoc, 3Clust, and DSDM, Revised Selected Papers
Number of pages12
ISBN (Print)9783642367779
Publication statusPublished - 5 Mar 2013
Externally publishedYes
EventPAKDD International Workshops 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)
Volume7769 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePAKDD International Workshops 2012
CityKuala Lumpur


  • Instance Ranking
  • KNN
  • ML.KNN
  • Multi-label Classification

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