...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics
【24h】

A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics

机译：一种新的混合萤火虫算法和粒子群优化，用于调谐参数估计在惩罚支持向量机中的应用中的化学计量学

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In quantitative structure-activity relationship (QSAR) classification, descriptor selection is one of the most important topics in the chemometrics. The selection of descriptors can be considered to be a variable selection problem that aims to find a small subset of descriptors that has the most discriminative information for the classification target. Penalized support vector machine (PSVM) proved its effectiveness by creating a strong classifier that combines the advantages of the support vector machine and the penalization. PSVM with Ll-norm and smoothly clipped absolute deviation (SCAD) penalty is the most widely used methods. However, the efficiency of PSVM with these penalties depends on appropriately choosing the tuning parameter which is involved in both penalties. Hybrid metaheuristics algorithms are of the most interesting recent trends in optimization to escape from the trapped in the local optimal. In this paper, a new hybrid firefly algorithm and particle swarm optimization is proposed to determine the tuning parameter in PSVM. Our proposed algorithm can efficiently exploit the strong points of both firefly and particle swarm algorithms in finding the most relevant descriptors with high classification performance. The experimental results on four benchmark QSAR datasets show the superior performance of the proposed algorithm in terms of classification accuracy and the number of selected descriptors compared with other competitor methods.
机译：在定量结构 - 活动关系（QSAR）分类中，描述符选择是化学计量学中最重要的主题之一。描述符的选择可以被认为是一个可变选择问题，其旨在找到具有对分类目标的最辨别信息的小描述符的小子集。受到惩罚的支持向量机（PSVM）通过创建强大的分类器来证明其有效性，该分类器结合了支持向量机和惩罚的优势。 PSVM具有LL-NARM和平滑剪裁的绝对偏差（SCAD）惩罚是最广泛使用的方法。 However, the efficiency of PSVM with these penalties depends on appropriately choosing the tuning parameter which is involved in both penalties.杂交地养算法是最近最有趣的优化趋势，以逃离当地最佳的陷阱。在本文中，提出了一种新的混合萤火虫算法和粒子群优化来确定PSVM中的调谐参数。我们所提出的算法可以有效地利用萤火虫和粒子群算法的强点来找到具有高分类性能的最相关的描述符。与其他竞争对手方法相比，四个基准QSAR数据集的实验结果显示了在分类准确性和所选描述符的数量方面的卓越性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号