Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Boolean Feature Discovery in Empirical Learning
Machine Learning
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
ECML '93 Proceedings of the 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
An Evaluation of Approaches to Classification Rule Selection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On pruning and tuning rules for associative classifiers
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Statistical Identification of Key Phrases for Text Classification
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Entropy-based associative classification algorithm for mining manufacturing data
International Journal of Computer Integrated Manufacturing
The Mahalanobis-Taguchi system - Neural network algorithm for data-mining in dynamic environments
Expert Systems with Applications: An International Journal
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
Effectiveness of fuzzy discretization for class association rule-based classification
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Hybrid DIAAF/RS: statistical textual feature selection for language-independent text classification
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Building a highly-compact and accurate associative classifier
Applied Intelligence
Classification inductive rule learning with negated features
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Using a fuzzy association rule mining approach to identify the financial data association
Expert Systems with Applications: An International Journal
PISA: A framework for multiagent classification using argumentation
Data & Knowledge Engineering
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Information Sciences: an International Journal
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
International Journal of Applied Metaheuristic Computing
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Classification Association Rule Mining (CARM) systems operate by applying an Association Rule Mining (ARM) method to obtain classification rules from a training set of previously classified data. The rules thus generated will be influenced by the choice of ARM parameters employed by the algorithm (typically support and confidence threshold values). In this paper we examine the effect that this choice has on the predictive accuracy of CARM methods. We show that the accuracy can almost always be improved by a suitable choice of parameters, and describe a hill-climbing method for finding the best parameter settings. We also demonstrate that the proposed hill-climbing method is most effective when coupled with a fast CARM algorithm such as the TFPC algorithm which is also described.