Text categorization using an ensemble classifier based on a mean co-association matrix

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Luís Moreira-Matias (Author) FEUP; João Mendes-Moreira (Author) FEUP; João Gama (Author) FEP; Pavel Brazdil (Author) FEP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 525-539

8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012

Berlin, 13 July 2012 through 20 July 2012

Indexing

ISI Web of Science

Scopus - 8 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences

Other information

Authenticus ID: P-008-5GY

DOI: 10.1007/978-3-642-31537-4_41

Abstract (EN): Text Categorization (TC) has attracted the attention of the research community in the last decade. Algorithms like Support Vector Machines, Naïve Bayes or k Nearest Neighbors have been used with good performance, confirmed by several comparative studies. Recently, several ensemble classifiers were also introduced in TC. However, many of those can only provide a category for a given new sample. Instead, in this paper, we propose a methodology - MECAC - to build an ensemble of classifiers that has two advantages to other ensemble methods: 1) it can be run using parallel computing, saving processing time and 2) it can extract important statistics from the obtained clusters. It uses the mean co-association matrix to solve binary TC problems. Our experiments revealed that our framework performed, on average, 2.04% better than the best individual classifier on the tested datasets. These results were statistically validated for a significance level of 0.05 using the Friedman Test. © 2012 Springer-Verlag.

Language: English

Type (Professor's evaluation): Scientific