Accession Number : ADA327980

Title :   An Evaluation of Statistical Approaches to Text Categorization,

Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE

Personal Author(s) : Yang, Yiming

PDF Url : ADA327980

Report Date : 10 APR 1997

Pagination or Media Count : 12

Abstract : This paper is a comparative study of test categorization methods. Fourteen methods are investigated, based on previously published results and newly obtained results from additional experiments. Corps biases in commonly used document collection are examined using the performance of three classifiers. Problems in previously published experiments are analyzed, and the results of flawed experiments are excluded from the cross-method evaluation. As a result, eleven out of the fourteen methods are remained. A k-nearest neighbor (kNN) classifier was chosen for the performance baseline on several collections; on each collection, the performance scores of other methods were normalized using the score of kNN. This provides a common basis for a global observation on methods whose results are only available on individual collections. Widrow-Hoff, k-nearest neighbor, neural networks and the Linear Least Squares Fit mapping are the top-performing classifiers, while the Rocchio approaches had relatively poor results compared to the other learning methods. KNN is the only learning method that has scaled to the full domain of MEDLINE categories, showing a graceful behavior when the target space grows from the level of one hundred categories to a level of tens of thousands

Descriptors :   *EXPERT SYSTEMS, *TEXT PROCESSING, DATA BASES, ALGORITHMS, NEURAL NETS, LEARNING MACHINES, REGRESSION ANALYSIS, LEAST SQUARES METHOD.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE