By Gisele L. Pappa
Data mining is a really lively examine sector with many winning real-world app- cations. It contains a collection of strategies and techniques used to extract attention-grabbing or necessary wisdom (or styles) from real-world datasets, delivering worthy help for determination making in undefined, enterprise, govt, and technological know-how. even supposing there are already many varieties of knowledge mining algorithms to be had within the literature, it's nonetheless dif cult for clients to decide on the very best facts mining set of rules for his or her specific facts mining challenge. additionally, information mining al- rithms were manually designed; consequently they comprise human biases and personal tastes. This booklet proposes a brand new method of the layout of information mining algorithms. - stead of counting on the gradual and advert hoc technique of guide set of rules layout, this booklet proposes systematically automating the layout of information mining algorithms with an evolutionary computation strategy. extra accurately, we advise a genetic p- gramming process (a kind of evolutionary computation process 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 ideas from info. We specialize in genetic programming during this e-book since it is the paradigmatic form of desktop studying process for automating the iteration of courses and since it has the benefit of acting an international seek within the area of candidate ideas (data mining algorithms in our case), yet in precept different kinds of seek equipment for this job may be investigated within the future.
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Extra resources for Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
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. In this case Fig. 3(c) represents a partitioning scheme that is more likely to maximize classification accuracy on unseen test examples than the partitioning in Fig. 3(b), because the latter would be underfitting the training data. 3 On the Comprehensibility of Discovered Knowledge Although many data mining research projects use a measure of classification algorithm performance based only on predictive accuracy, it is accepted by many researchers and practitioners that, in many application domains, the comprehensibility of the knowledge (or patterns) discovered by a classification algorithm is another important evaluation criterion.
This is undesirable, because R2 is not a statistically reliable rule, being based on such a small number of covered examples. 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.
As mentioned before, in this book we are mainly interested in the discovery of high-level, easy-to-interpret classification rules of the form if (conditions) then (class), where the rule antecedent (the if part) specifies a set of conditions referring to predictor attribute values, and the rule consequent specifies the class predicted by the rule to any example that satisfies the conditions in the rule antecedent. A simple example of a classification rule would be if (Salary >100,000 euros and Has debt = no) then (Credit = good).