Automating the Design of Data Mining Algorithms: An by Gisele L. Pappa

By Gisele L. Pappa

Data mining is a truly lively examine quarter with many winning real-world app- cations. It comprises a collection of suggestions and techniques used to extract fascinating or beneficial wisdom (or styles) from real-world datasets, delivering necessary help for selection making in undefined, company, executive, and technological know-how. even if there are already many sorts of information mining algorithms on hand within the literature, it's nonetheless dif cult for clients to settle on the absolute best facts mining set of rules for his or her specific info mining challenge. furthermore, facts mining al- rithms were manually designed; for that reason they comprise human biases and personal tastes. This publication proposes a brand new method of the layout of information mining algorithms. - stead of hoping on the sluggish and advert hoc means of guide set of rules layout, this publication proposes systematically automating the layout of information mining algorithms with an evolutionary computation method. extra accurately, we recommend a genetic p- gramming process (a kind of evolutionary computation strategy that evolves c- puter courses) to automate the layout of rule induction algorithms, a kind of cl- si cation process that discovers a suite of classi cation ideas from information. We specialise in genetic programming during this booklet since it is the paradigmatic form of computing device studying technique for automating the new release of courses and since it has the good thing about appearing a world seek within the house of candidate suggestions (data mining algorithms in our case), yet in precept different kinds of seek tools for this activity might be investigated within the future.

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Examples belonging to the class predicted by the rule) in the training set. 3: LearnOneRule(R) bestRule = R candidateRules = 0/ candidateRules = candidateRules ∪ bestRule while candidateRules = 0/ do newCandidateRules= 0/ for each candidateRule CR do Refine CR Evaluate CR if Refine Rule Stopping Criterion not satisfied then newCandidateRules = newCandidateRules ∪ CR if CR is better than bestRule then bestRule = CR candidateRules = Select b best rules in newCandidateRules return bestRule the set of examples covered by the P-rules.

In order to overcome this problem with the confidence measure, the Laplace estimation (or “correction”) measure was introduced, and it is defined in Eq. 2). In Eq. 2), nClasses is the number of classes available in the training set. Using this heuristic, rules with apparently high confidence but very small statistical support are penalized. Consider the previously mentioned rules R1 and R2 in a two-class problem. 75 for R2 . 4 Rule Induction via the Sequential Covering Approach 35 be preferred over R2 , as it should be.

The main differences between I-REP and Ripper lie in Ripper’s optimization process, which is absent in I-REP, and on the heuristics used for pruning rules and stopping the addition of rules to the rule set. The optimization process considers each rule in the current rule set in turn, and creates two alternative rules from them: a replacement rule and a revision rule [22]. After that, a decision is made on whether the model should keep the original rule, the replacement, or the revision rule, based on the minimum description length criterion.

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