A novel memetic algorithm for discovering knowledge in binary and multi class predictions based on support vector machine
A novel memetic algorithm for discovering knowledge in binary and multi class predictions based on support vector machine
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DOI：
10.1016/j.asoc.2016.08.038
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发表时间：
2016-12-01
影响因子：
8.7
通讯作者：
Geetha, S.
中科院分区：
文献类型：
--
作者：
Sasikala, S.;Balamurugan, S. Appavu alias;Geetha, S.
在分类中，数据集的每个特征都是预测精度的重要贡献者，并影响模型构建成本。为了提取用于预测的优先特征，设计了合适的特征选择器。提出了一种基于模因的特征选择模型-Shapely Value Embedded Genetic Algorithm（SVEGA）。每个功能对预测的相关性是通过组装遗传算法与形状值的措施从SVEGA检索。然后使用具有不同内核配置的支持向量机（SVM）对11 + 11个基准数据集（二进制类和多类）进行评估。最后，将SVEGA-SVM与现有的特征选择模型进行了对比分析。实验结果与建议的设置提供了强大的结果，因此证明它是一种有效的方法，通过特征选择发现知识，提高分类精度相比，传统的方法。(C)2016爱思唯尔B. V.保留所有权利。
In classification, every feature of the data set is an important contributor towards prediction accuracy and affects the model building cost. To extract the priority features for prediction, a suitable feature selector is schemed. This paper proposes a novel memetic based feature selection model named Shapely Value Embedded Genetic Algorithm (SVEGA). The relevance of each feature towards prediction is measured by assembling genetic algorithms with shapely value measures retrieved from SVEGA. The obtained results are then evaluated using Support Vector Machine (SVM) with different kernel configurations on 11 + 11 benchmark datasets (both binary class and multi class). Eventually, a contrasting analysis is done between SVEGA-SVM and other existing feature selection models. The experimental results with the proposed setup provides robust outcome; hence proving it to be an efficient approach for discovering knowledge via feature selection with improved classification accuracy compared to conventional methods. (C) 2016 Elsevier B.V. All rights reserved.