دانلود مقاله ISI انگلیسی شماره 46818
عنوان فارسی مقاله

LAD چندمنظوره سلسله مراتبی بر اساس درخت OVA باینری با استفاده از الگوریتم ژنتیک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46818 2015 12 صفحه PDF سفارش دهید 9490 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Hierarchical multi-class LAD based on OvA-binary tree using genetic algorithm
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 21, 30 November 2015, Pages 8134–8145

کلمات کلیدی
طبقه بندی چند منظوره سلسله مراتبی - تجزیه و تحلیل منطقی داده ها - الگوریتم ژنتیک - دقت طبقه بندی
پیش نمایش مقاله
پیش نمایش مقاله LAD چندمنظوره سلسله مراتبی بر اساس درخت OVA باینری با استفاده از الگوریتم ژنتیک

چکیده انگلیسی

Recently, logical analysis of data (LAD) using a classifier based on a linear combination of patterns has been introduced, providing high classification accuracy and pattern-based interpretability on classification results. However, it is known that most of LAD-based multi-classification algorithms have conflicts between classification accuracy and computational complexity because they are based on class decomposition method such as one versus all or one versus one. Furthermore, it is difficult to explain the decision rule in the classification procedure because they only use the final scores calculated by classifiers. To overcome this issue, in this paper, we propose a hierarchical multi-class classification method using LAD based on a one versus all (OvA)-binary tree, called hierarchical multi-class LAD (HMC-LAD). It constructs an OvA-binary tree by partitioning a node with K(⩾2)K(⩾2) classes into two sub-nodes by identifying one distinct class from the remaining (K-1)(K-1) classes repeatedly. Specifically, we suggest a node partition method for constructing an efficient OvA-binary tree, genetic algorithm for generating patterns for a node under consideration, and OvA-binary tree exploration method for performing multi-class classification. Through a numerical experiment using benchmark datasets from the UCI machine-learning repository, we confirm that (i) the suggested node partition method is efficient compared to a random partition method, and (ii) the classification performance of HMC-LAD is superior to existing multi-class LAD algorithms and other supervised learning approaches. The proposed HMC-LAD can be applied to expert and intelligent systems to effectively categorize large amount of data in knowledge base and perform inference for decision making.

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