Logistic regression modeling for context-based classification

Author

Brzezinski, Jack R. ; Knafl, George J.

Author_Institution

Sch. of Comput. Sci., Telecommun. & Inf. Syst., DePaul Univ., Chicago, IL, USA

fYear

1999

fDate

1999

Firstpage

755

Lastpage

759

Abstract

We focus on a machine learning approach to the concept/document classification for IR. We apply a logistic regression-based algorithm to three types of classification tasks: binary classification, multiple classification and classification into a hierarchy. At this stage, for our experiments we use a set of 150 topics from the TIPSTER collection. We develop heuristics as to how to build a logistic regression model for high dimensional, sparse data sets. This research describes work in progress

Keywords

classification; information retrieval; learning (artificial intelligence); statistical analysis; TIPSTER collection; binary classification; concept document classification; context-based classification; experiments; heuristics; hierarchy; information retrieval; logistic regression modeling; machine learning; multiple classification; sparse data sets; Classification algorithms; Computer science; Context modeling; Electronic mail; Information systems; Lifting equipment; Linear regression; Logistics; Machine learning; Machine learning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Database and Expert Systems Applications, 1999. Proceedings. Tenth International Workshop on

Conference_Location

Florence

Print_ISBN

0-7695-0281-4

Type

conf

DOI

10.1109/DEXA.1999.795279

Filename

795279