عنوان مقاله
انتخاب بهینه دسته بندهای دسته جمعی با استفاده از معیارهای مهارت و تنوع دسته بندهای پایه
فهرست مطالب
مقدمه
چارچوب نظری
سیستم های انتخاب دسته جمعی پویا
آزمایشات
نتیجه گیری
بخشی از مقاله
هدف ما تعیین ویژگی های زیر است
معیار مهارت C(𝜑𝑙|𝑥) هر دسته بند پایه (l=1,2,…L) که مهارت دسته بند 𝜑1 به عبارتی قابلیت فعالیت درست ( دسته بندی درست) در نقطه x∈𝑋 را ارزیابی می کند.
معیار تنوع D(𝜑𝐸|𝑥) هر مجموعه از دسته بندهای پایه 𝜑𝐸 مستقل از خطاهای رخ داده توسط دسته بندهای عضو در نقطه x∈𝑋 در نظر گرفته شده است.
کلمات کلیدی:
Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers Rafal Lysiak n , Marek Kurzynski, Tomasz Woloszynski Wroclaw University of Technology, Department of Systems and Computer Networks, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland article info Article history: Received 9 May 2012 Received in revised form 30 November 2012 Accepted 10 January 2013 Available online 9 August 2013 Keywords: Dynamic ensemble selection Classifier competence Diversity measure Simulated annealing abstract In this paper, a new probabilistic model using measures of classifier competence and diversity is proposed. The multiple classifier system (MCS) based on the dynamic ensemble selection scheme was constructed using both developed measures. Two different optimization problems of ensemble selection are defined and a solution based on the simulated annealing algorithm is presented. The influence of minimum value of competence and diversity in the ensemble on classification performance was investigated. The effectiveness of the proposed dynamic selection methods and the influence of both measures were tested using seven databases taken from the UCI Machine Learning Repository and the StatLib statistical dataset. Two types of ensembles were used: homogeneous or heterogeneous. The results show that the use of diversity positively affects the quality of classification. In addition, cases have been identified in which the use of this measure has the greatest impact on quality. & 2013 Elsevier B.V. All rights reserved. 1. Introduction At present, in identification and classification, the Multiple Classification Systems (MCS) are very strongly developed, mostly because of the fact that committee, also known as an ensemble, can outperform its members [1]. It is well known that one of the most important steps in the design of MCS is the ensemble selection and the other is combining their answers. Currently, MCS which are using Dynamic Ensemble Selection (DES) schemes are becoming increasingly popular. The DES method is based on dynamic selection of classifiers for a classifying object due to its feature vector. In other words, the MCS each time select the new ensemble (called dynamic way) for each recognition object depending on the characteristics describing the object. Most DES schemes use the concept of classifier competence on a defined neighbourhood or region [2], such as the local accuracy estimation [3–5], Bayes confidence measure [6], multiple classifier behavior [7] or probabilistic model [8], among others. Note that even the best MCS will not be able to outperform its members if classifiers in the team are identical. The ideal situation is when classifiers in the ensemble are the most competent and where the probability of correct classification for the recognition object is the greatest, but are possibly different from each other at the same time. It is popular to use the diversity measure to select such a committee. In the literature, there are many approaches to defining and determining diversification [9]. In this paper, the authors tried to create such a model which will select the best classifiers (most competent) while trying to differentiate their wrong answers. There are examples which show that the use of measure of diversification positively affects the performance of the whole recognition process [10]. In this paper, a novel model has been presented which uses both competence and diversity. In this way, we obtained a hybrid architecture [11] which uses two independent measures. Furthermore, two types of optimization problems were considered. Problem of classifiers selection, because of the criteria and constraints, is solved using simulated annealing [12]. Methods for calculating classifier competence and diversity using a probabilistic model are based on the original concept of a randomized reference classifier (RRC) [8], which – on average – acts like the evaluated classifier. The competence of a classifier is calculated as the probability of correct classification of the respective RRC, and the class-dependent error probabilities of RRC are used for determining the diversity measure, which evaluates the difference of incorrect outputs of classifiers [13,14]. The proposed methods are novel because they take under consideration the competence and diversity measures at the same time during the selection process. The motivation of our work on the development of the algorithm described in this paper were the results of previous research [15]. It was the first time that both measures were combined with each other, and the results were promising. It should be noted that previously used algorithms, selecting subsets of classifiers, which are involved in the recognition process, were
