Download Data Mining and Knowledge Discovery with Evolutionary by Alex A. Freitas PDF

By Alex A. Freitas

This e-book integrates parts of laptop technology, particularly info mining and evolutionary algorithms. either those parts became more and more well known within the previous few years, and their integration is presently a space of energetic examine. normally, info mining contains extracting wisdom from information. during this e-book we rather emphasize the significance of learning understandable and engaging wisdom, that's most likely precious to the reader for clever selection making. In a nutshell, the incentive for using evolutionary algorithms to info mining is that evolutionary algorithms are strong seek tools which practice an international seek within the area of candidate ideas (rules or one other kind of wisdom representation). against this, so much rule induction equipment practice an area, grasping seek within the area of candidate principles. Intuitively, the worldwide seek of evolutionary algorithms can notice attention-grabbing principles and styles that will be ignored by way of the grasping search.
This booklet provides a accomplished evaluation of easy techniques on either facts mining and evolutionary algorithms and discusses major advances within the integration of those components. it truly is self-contained, explaining either easy suggestions and complicated topics.

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In essence, the user specifies her/his general impressions about data relationships in the application domain. 28 2 Data Mining Tasks and Concepts These general impressions are specified in an IF-THEN, prediction-rule-like format. For instance, a given user might specify the following general impression: IF (Salary = high) THEN (Credit= good). Note that this is a general impression in the sense that it is quite vague (fuzzy), different from a reasonably-precise rule such as: IF (Salary > $50,000 ) THEN (Credit = good).

Parameter setting must be done by using the training set only. Another methodological mistake involves the issue of running a stochastic classification algorithm many times, with different values of random seed used for initializing the algorithm. This is typically done in the context of evolutionary algorithms (EAs). Multiple runs of an EA are of course desirable, to better validate the results produced by this kind of stochastic algorithm. However, in the 26 2 Data Mining Tasks and Concepts context of data mining and prediction in general, a methodological mistake is made when only the accuracy rate (on the test set) of the best run of the EA, among all runs of the EA, is reported.

The measured classification accuracy rate is an estimate of the true classification accuracy rate of the algorithm over the entire unknown distribution of data instances. Theoretical or academic research usually stops at the end of this phase, but in real-world applications of data mining there is usually a third phase: in the future the algorithm will be used to classify truly new, unknown-class data instances - instances which were not available in the original data set and whose class is truly unknown for the user.

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