On support thresholds in associative classification
Proceedings of the 2004 ACM symposium on Applied computing
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Boosting an Associative Classifier
IEEE Transactions on Knowledge and Data Engineering
Associative text categorization exploiting negated words
Proceedings of the 2006 ACM symposium on Applied computing
A greedy classification algorithm based on association rule
Applied Soft Computing
A review of associative classification mining
The Knowledge Engineering Review
Design of a two-stage fuzzy classification model
Expert Systems with Applications: An International Journal
An Agent-Oriented Data Mining Framework for Mass Customization in the Automotive Industry
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Intelligent file scoring system for malware detection from the gray list
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance evaluation of classification methods in cultural modeling
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
ACIK: association classifier based on itemset kernel
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
CIMDS: adapting postprocessing techniques of associative classification for malware detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Journal of Intelligent Information Systems
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Improving the performance of association classifiers by rule prioritization
Knowledge-Based Systems
Mining actionable behavioral rules
Decision Support Systems
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Associative classification is a promising technique forthe generation of highly precise classifiers. Previous workspropose several clever techniques to prune the huge set ofgenerated rules, with the twofold aim of selecting a smallset of high quality rules, and reducing the chance of overfitting.In this paper, we argue that pruning should be reducedto a minimum and that the availability of a large rule basemay improve the precision of the classifier, without affectingits performance. In L3 (Live and Let Live), a new algorithmfor associative classification, a lazy pruning technique iterativelydiscards all rules that only yield wrong case classifications.Classification is performed in two steps. Initially, ruleswhich have already correctly classified at least one trainingcase, sorted by confidence, are considered. If the caseis still unclassified, the remaining rules (unused during thetraining phase) are considered, again sorted by confidence.Extensive experiments on 26 databases from the UCImachine learning database repository show that L3 improvesthe classification precision with respect to previousapproaches.