Back to results

University of Illinois at Urbana-Champaign

Towards Accurate and Efficient Classification: A Discriminative and Frequent Pattern-Based Approach

Abstract

dc:description

In conclusion, the framework of discriminative frequent pattern-based classification could lead to a highly accurate, efficient and interpretable classifier on complex data. The pattern-based classification technique would have great impact in a wide range of applications including text categorization, chemical compound classification, software behavior analysis and so on.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Computer Science
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Cheng, Hong
Contributors dc:contributor
  • Han, Jiawei

Subjects

dc:subject × 1

Rights

Language dc:language
eng

Identifiers

dc:identifier.*
Identifier
(MiAaPQ)AAI3337732
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/81825

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Cheng, Hong. Towards Accurate and Efficient Classification: A Discriminative and Frequent Pattern-Based Approach. Dissertation thesis, University of Illinois at Urbana-Champaign, 2015. http://hdl.handle.net/2142/81825