C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
A Study on the Performance of Large Bayes Classifier
ECML '00 Proceedings of the 11th European Conference on 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
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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Recent studies in classification have proposed ways of exploiting the association rule mining paradigm. These studies have performed extensive experiments to show their techniques to be both efficient and accurate. However, existing studies in this paradigm either do not provide any theoretical justification behind their approaches or assume independence between some parameters. In this work, we propose a new classifier based on association rule mining. Our classifier rests on the maximum entropy principle for its statistical basis and does not assume any independence not inferred from the given dataset. We use the classical generalized iterative scaling algorithm (GIS) to create our classification model. We show that GIS fails in some cases when itemsets are used as features and provide modifications to rectify this problem. We show that this modified GIS runs much faster than the original GIS. We also describe techniques to make GIS tractable for large feature spaces – we provide a new technique to divide a feature space into independent clusters each of which can be handled separately. Our experimental results show that our classifier is generally more accurate than the existing classification methods.