An effective and interpretable method for document classification

As the number of documents has been rapidly increasing in recent time, automatic text categorization is becoming a more important and fundamental task in information retrieval and text mining. Accuracy and interpretability are two important aspects of a text classifier. While the accuracy of a class...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge and information systems 2017-03, Vol.50 (3), p.763-793
Main Authors: Van Linh, Ngo, Anh, Nguyen Kim, Than, Khoat, Dang, Chien Nguyen
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Bayesian analysis
Classification
Classifiers
Computer Science
Data mining
Data Mining and Knowledge Discovery
Database Management
Dirichlet problem
Documents
Information retrieval
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
Information technology
IT in Business
Learning
Methods
Regular Paper
Studies
Support vector machines
Task complexity
Tasks
Text categorization
Texts
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As the number of documents has been rapidly increasing in recent time, automatic text categorization is becoming a more important and fundamental task in information retrieval and text mining. Accuracy and interpretability are two important aspects of a text classifier. While the accuracy of a classifier measures the ability to correctly classify unseen data, interpretability is the ability of the classifier to be understood by humans and provide reasons why each data instance is assigned to a label. This paper proposes an interpretable classification method by exploiting the Dirichlet process mixture model of von Mises–Fisher distributions for directional data. By using the labeled information of the training data explicitly and determining automatically the number of topics for each class, the learned topics are coherent, relevant and discriminative. They help interpret as well as distinguish classes. Our experimental results showed the advantages of our approach in terms of separability, interpretability and effectiveness in classification task of datasets with high dimension and complex distribution. Our method is highly competitive with state-of-the-art approaches.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-016-0956-6