Classification with maximum entropy modeling of predictive association rules

Authors:
Hieu X. Phan;Minh L. Nguyen;S. Horiguchi;Bao T. Ho;Y. Inoguchi
Affiliations:
Japan Advanced Institute of Science and Technology, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Ishikawa, Japan;Tohoku University, Sendai, Japan;Japan Advanced Institute of Science and Technology, Ishikawa, Japan;Japan Advanced Institute of Science and Technology, Ishikawa, Japan
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

Citing 14
Cited 0

On the limited memory BFGS method for large scale optimization

Mathematical Programming: Series A and B
C4.5: programs for machine learning

C4.5: programs for machine learning
A maximum entropy approach to natural language processing

Computational Linguistics
Inducing Features of Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient mining of emerging patterns: discovering trends and differences

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The CN2 Induction Algorithm

Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CAEP: Classification by Aggregating Emerging Patterns

DS '99 Proceedings of the Second International Conference on Discovery Science
A maximum entropy approach to named entity recognition

A maximum entropy approach to named entity recognition
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
ART: A Hybrid Classification Model

Machine Learning
A comparison of algorithms for maximum entropy parameter estimation

COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new classification model in which a classifier is built upon predictive association rules (PARs) and the maximum entropy principle (maxent). In this model, PARs can be seen as confident statistical patterns discovered from training data with strong dependencies and correlations among data items. Maxent, on the other hand, is an approach to build an estimated distribution having maximum entropy while obeying a potentially large number of useful features observed in empirical data. The underlying idea of our model is that PARs have suitable characteristics to serve as features for maxent. As a result, our classifier can take advantage of both the useful correlation and confidence of PARs as well as the strong statistical modeling capability of maxent. The experimental results show that our model can achieve significantly higher accuracy in comparison with the previous methods.