دانلود مقاله ISI انگلیسی شماره 78807
عنوان فارسی مقاله

بهینه سازی چند هدفه از طبقه بندی ها با استفاده از الگوریتم های تکاملی مبتنی بر بدنه محدب 3D

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78807 2016 25 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms
منبع

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

Journal : Information Sciences, Volumes 367–368, 1 November 2016, Pages 80–104

کلمات کلیدی
بدنه محدب؛ تقسیم بندی؛ بهینه سازی چندمنظوره تکاملی؛ صرفه جویی؛ تجزیه و تحلیل ROC؛ فیلترهای ضد هرزنامه
پیش نمایش مقاله
پیش نمایش مقاله بهینه سازی چند هدفه از طبقه بندی ها با استفاده از الگوریتم های تکاملی مبتنی بر بدنه محدب 3D

چکیده انگلیسی

The receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are frequently used in the machine learning community to analyze the performance of binary classifiers. Recently, the convex-hull-based multiobjective genetic programming algorithm was proposed and successfully applied to maximize the convex hull area for binary classification problems by minimizing false positive rate and maximizing true positive rate at the same time using indicator-based evolutionary algorithms. The area under the ROC curve was used for the performance assessment and to guide the search. Here we extend this research and propose two major advancements: Firstly we formulate the algorithm in detection error tradeoff space, minimizing false positives and false negatives, with the advantage that misclassification cost tradeoff can be assessed directly. Secondly, we add complexity as an objective function, which gives rise to a 3D objective space (as opposed to a 2D previous ROC space). A domain specific performance indicator for 3D Pareto front approximations, the volume above DET surface, is introduced, and used to guide the indicator-based evolutionary algorithm to find optimal approximation sets. We assess the performance of the new algorithm on designed theoretical problems with different geometries of Pareto fronts and DET surfaces, and two application-oriented benchmarks: (1) Designing spam filters with low numbers of false rejects, false accepts, and low computational cost using rule ensembles, and (2) finding sparse neural networks for binary classification of test data from the UCI machine learning benchmark. The results show a high performance of the new algorithm as compared to conventional methods for multicriteria optimization.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.