International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
C4.5: programs for machine learning
C4.5: programs for machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning
IEEE Transactions on Knowledge and Data Engineering
Rule Combination in Inductive Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Controlled Redundancy in Incremental Rule Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Proposition of the quality measure for the probabilistic decision support system
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Application of the confidence measure in knowledge acquisition process
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Knowledge source confidence measure applied to a rule-based recognition system
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Combining classifiers under probabilistic models: experimental comparative analysis of methods
Expert Systems: The Journal of Knowledge Engineering
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This paper addresses an important problem related to the use ofinduction systems in analyzing real world data. The problem is thequality and reliability of the rules generated by the systems.~Wediscuss the significance of having a reliable and efficient rule quality measure. Such a measure can provide useful support ininterpreting, ranking and applying the rules generated by aninduction system. A number of rule quality and statistical measuresare selected from the literature and their performance is evaluatedon four sets of semiconductor data. The primary goal of thistesting and evaluation has been to investigate the performance ofthese quality measures based on: (i) accuracy, (ii) coverage, (iii)positive error ratio, and (iv) negative error ratio of the ruleselected by each measure. Moreover, the sensitivity of these qualitymeasures to different data distributions is examined. Inconclusion, we recommend Cohen‘s statistic as being the best qualitymeasure examined for the domain. Finally, we explain some future workto be done in this area.