QAR-CIP-NSGA-II: یک الگوریتم تکاملی چند هدفه جدید به قوانین انجمن کمی معدن
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|78932||2014||28 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Sciences, Volume 258, 10 February 2014, Pages 1–28
Some researchers have framed the extraction of association rules as a multi-objective problem, jointly optimizing several measures to obtain a set with more interesting and accurate rules. In this paper, we propose a new multi-objective evolutionary model which maximizes the comprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy. To accomplish this, the model extends the well-known Multi-objective Evolutionary Algorithm Non-dominated Sorting Genetic Algorithm II to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule. Moreover, this proposal introduces an external population and a restarting process to the evolutionary model in order to store all the nondominated rules found and improve the diversity of the rule set obtained. The results obtained over real-world datasets demonstrate the effectiveness of the proposed approach.