By Gisele L. Pappa
Data mining is a really lively learn region with many profitable real-world app- cations. It involves a collection of suggestions and strategies used to extract attention-grabbing or important wisdom (or styles) from real-world datasets, delivering priceless help for selection making in undefined, company, govt, and technology. even supposing there are already many varieties of information mining algorithms to be had within the literature, it really is nonetheless dif cult for clients to settle on the very best facts mining set of rules for his or her specific facts mining challenge. moreover, information mining al- rithms were manually designed; accordingly they include human biases and personal tastes. This ebook proposes a brand new method of the layout of information mining algorithms. - stead of hoping on the gradual and advert hoc technique of guide set of rules layout, this e-book proposes systematically automating the layout of knowledge mining algorithms with an evolutionary computation technique. extra accurately, we suggest a genetic p- gramming method (a form of evolutionary computation approach that evolves c- puter courses) to automate the layout of rule induction algorithms, one of those cl- si cation approach that discovers a suite of classi cation ideas from info. We specialise in genetic programming during this ebook since it is the paradigmatic kind of computing device studying procedure for automating the iteration of courses and since it has the benefit of appearing an international seek within the area of candidate strategies (data mining algorithms in our case), yet in precept different varieties of seek tools for this activity will be investigated within the future.
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Additional resources for Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
18 2 Data Mining The area of meta-learning appeared as an alternative to help in choosing appropriate classification algorithms for specific datasets, as it is well known that no classification algorithm will perform well in all datasets. 6 summarizes the chapter. 2 The Classification Task of Data Mining This section provides an overview of basic concepts and issues involved in the classification task of data mining. A more detailed discussion can be found in several good books about the subject, including  and .
In the same manner, the block “Create an Initial Rule R” in Alg. ” The block “Evaluate CR” in Alg. ” Replacing building blocks in these basic algorithms by specific methods can create the majority of the existing sequential covering rule induction algorithms. This is possible because algorithms following the sequential covering approach usually differ from each other in four main ways: the representation of the candidate rules, the search mechanisms used to explore the space of the candidate rules, the way the candidate rules are evaluated, and the rule pruning method, although the last one can be absent [36, 78].
Examples of rule evaluation heuristics used by these algorithms are confidence, Laplace estimation, M-estimate, and LS content. Confidence (also known as precision or purity) is the simplest rule evaluation function and is described as in Eq. 1). 1) It is used by SWAP-1, and its main drawback is that it is prone to overfitting. 95), and a rule R2 covering two positive examples and no negative examples (confidence = 1). An algorithm choosing a rule based on the confidence measure will prefer R2 .
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