کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946156 1439272 2017 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
SVM-based multi-state-mapping approach for multi-class classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
SVM-based multi-state-mapping approach for multi-class classification
چکیده انگلیسی
Traditional SVM-based multi-class classification algorithms mainly adopt the strategy of mapping the data set with all classes into a single feature space via a kernel function, in which SVM is constructed for each decomposed binary classification problem. However, it is not always possible to find an appropriate kernel function to render all the classes distinguishable in a single feature space, since each class is always derived from different data distributions. Consequently, the performance is not always as good as expected. To improve the performance of multi-class classification, this paper proposes an improved approach, called multi-state-mapping (MSM) with SVM based on hierarchical architecture, which maps the data set with all classes into different feature spaces at the different states of the decomposition of a multi-class classification problem in terms of a binary tree architecture. We prove that the computational complexity of MSM at its worst lies between that of the one-against-all scheme and one-against-one scheme. Substantial experiments have been conducted on sixteen UCI data sets to show the performance of our method. The statistical results show that MSM outperforms state-of-the-art methods in terms of accuracy and standard deviation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 129, 1 August 2017, Pages 79-96
نویسندگان
, , ,