Please use this identifier to cite or link to this item: https://doi.org/10.1145/1148020.1148022
Title: A Maximal Figure-of-Merit (MFoM)-learning approach to robust classifier design for text categorization
Authors: Gao, S.
Wen, W.U.
Lee, C.-H.
Chua, T.-S. 
Keywords: Decision tree
Generalized probabilistic descent method
Information retrieval
Latent semantic indexing
Maximal figure-of-merit
Text categorization
Issue Date: 2006
Source: Gao, S.,Wen, W.U.,Lee, C.-H.,Chua, T.-S. (2006). A Maximal Figure-of-Merit (MFoM)-learning approach to robust classifier design for text categorization. ACM Transactions on Information Systems 24 (2) : 190-218. ScholarBank@NUS Repository. https://doi.org/10.1145/1148020.1148022
Abstract: We propose a maximal figure-of-merit (MFoM)-learning approach for robust classifier design, which directly optimizes performance metrics of interest for different target classifiers. The proposed approach, embedding the decision functions of classifiers and performance metrics into an overall training objective, learns the parameters of classifiers in a decision-feedback manner to effectively take into account both positive and negative training samples, thereby reducing the required size of positive training data. It has three desirable properties: (a) it is a performance metric, oriented learning; (b) the optimized metric is consistent in both training and evaluation sets; and (c) it is more robust and less sensitive to data variation, and can handle insufficient training data scenarios. We evaluate it on a text categorization task using the Reuters-21578 dataset. Training an F 1-based binary tree classifier using MFoM, we observed significantly improved performance and enhanced robustness compared to the baseline and SVM, especially for categories with insufficient training samples. The generality for designing other metrics-based classifiers is also demonstrated by comparing precision, recall, and F 1-based classifiers. The results clearly show consistency of performance between the training and evaluation stages for each classifier, and MFoM optimizes the chosen metric. © 2006 ACM.
Source Title: ACM Transactions on Information Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/39600
ISSN: 10468188
DOI: 10.1145/1148020.1148022
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

28
checked on Nov 17, 2017

WEB OF SCIENCETM
Citations

21
checked on Oct 31, 2017

Page view(s)

68
checked on Nov 19, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.