Classification approach towards ranking and sorting problems

Shyamsundar Rajaram, Ashutosh Garg, Xiang Sean Zhou, Thomas S Huang

Research output: Contribution to journalConference article

Abstract

Recently, ranking and sorting problems have attracted the attention of researchers in the machine learning community. By ranking, we refer to categorizing examples into one of K categories. On the other hand, sorting refers to coming up with the ordering of the data that agrees with some ground truth preference function. As against standard approaches of treating ranking as a multiclass classification problem, in this paper we argue that ranking/sorting problems can be solved by exploiting the inherent structure present in data. We present efficient formulations that enable the use of standard binary classification algorithms to solve these problems, however the structure is still captured in our formulations. We further show that our approach subsumes the various approaches that were developed in the past. We evaluate our algorithm on both synthetic datasets and for a real world image processing problem. The results obtained demonstrate the superiority of our algorithm over multiclass classification and other similar approaches for ranking/sorting data.

Original languageEnglish (US)
Pages (from-to)301-312
Number of pages12
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2837
StatePublished - Dec 1 2003
Event14th European Conference on Machine Learning - Cavtat-Dubrovnik, Croatia
Duration: Sep 22 2003Sep 26 2003

Fingerprint

Sorting
Ranking
Multi-class Classification
Binary Classification
Formulation
Learning systems
Classification Algorithm
Image processing
Classification Problems
Image Processing
Machine Learning
Evaluate
Demonstrate
Standards

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Classification approach towards ranking and sorting problems. / Rajaram, Shyamsundar; Garg, Ashutosh; Zhou, Xiang Sean; Huang, Thomas S.

In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Vol. 2837, 01.12.2003, p. 301-312.

Research output: Contribution to journalConference article

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