Classification and novel class detection in data streams with active mining

Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, concept-evolution, and limited labeled data. Most of the existing data stream classification techniques address only the infinite length and concept-drift problems. Our previous work, MineClass, addresses the concept-evolution problem in addition to addressing the infinite length and concept-drift problems. Concept-evolution occurs in the stream when novel classes arrive. However, most of the existing data stream classification techniques, including MineClass, require that all the instances in a data stream be labeled by human experts and become available for training. This assumption is impractical, since data labeling is both time consuming and costly. Therefore, it is impossible to label a majority of the data points in a high-speed data stream. This scarcity of labeled data naturally leads to poorly trained classifiers. ActMiner actively selects only those data points for labeling for which the expected classification error is high. Therefore, ActMiner extends MineClass, and addresses the limited labeled data problem in addition to addressing the other three problems. It outperforms the state-of-the-art data stream classification techniques that use ten times or more labeled data than ActMiner.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages311-324
Number of pages14
EditionPART 2
DOIs
StatePublished - 2010
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India
Duration: Jun 21 2010Jun 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6119 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
CountryIndia
CityHyderabad
Period6/21/106/24/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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