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

By Gisele L. Pappa

Data mining is a really energetic study sector with many profitable real-world app- cations. It includes a suite of options and strategies used to extract fascinating or worthy wisdom (or styles) from real-world datasets, supplying beneficial aid for selection making in undefined, company, govt, and technology. even if there are already many sorts of information mining algorithms on hand within the literature, it really is nonetheless dif cult for clients to decide on the very best information mining set of rules for his or her specific information mining challenge. moreover, info mining al- rithms were manually designed; for this reason they comprise human biases and personal tastes. This e-book proposes a brand new method of the layout of knowledge mining algorithms. - stead of hoping on the gradual and advert hoc strategy of guide set of rules layout, this booklet proposes systematically automating the layout of information mining algorithms with an evolutionary computation process. extra accurately, we advise a genetic p- gramming approach (a kind of evolutionary computation approach that evolves c- puter courses) to automate the layout of rule induction algorithms, a kind of cl- si cation approach that discovers a collection of classi cation principles from facts. We specialise in genetic programming during this booklet since it is the paradigmatic kind of computer studying process for automating the iteration of courses and since it has the benefit of acting a world seek within the house of candidate suggestions (data mining algorithms in our case), yet in precept different varieties of seek equipment for this activity can be investigated within the future.

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Rule lists are, in general, considered more difficult to understand than rule sets. This is because in order to comprehend a given rule in an ordered list all the previous rules must also be taken into consideration [19]. Since the knowledge generated by rule induction algorithms is supposed to be analyzed and validated by an expert, rules at the end of the list become very difficult to understand, particularly in very long lists. Hence, unordered rules are often favored over ordered ones when comprehensibility is a particularly important rule evaluation criterion.

This approach was attempted by some decision tree induction algorithms in the past, but there is no strong evidence of whether look-ahead improves or harms the performance of the algorithm. While Dong and Kothari [25] concluded that look-ahead produces better decision trees (using a nonparametric look-ahead method), Murthy and Salzberg [57] argued it can produce larger and less accurate trees (using a one-step look-ahead method). A more recent study by Esmeir and Markovich [27] used look-ahead for decision tree induction, and found that look-ahead produces better trees and higher accuracies, as long as a large amount of time is available.

If this is the case, Fig. 3(b) represents a partitioning scheme that is more likely to maximize classification accuracy on unseen test examples than the partitioning in Fig. 3(c). The reason is that the latter would be mistakenly creating a small partition that covers only two noisy training examples, in which case we would say the classification model is overfitting the training data. On the other hand, it is possible that those two examples are in reality true exceptions in the data, representing a valid (though rare) relationship between attributes in the training data that is likely to be true in the test set too.

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