首页> 外文会议>Artificial neural networks in pattern recognition >Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiers
【24h】

Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiers

机译:集成分类器的基于相关性和因果特征选择分析

获取原文
获取原文并翻译 | 示例

摘要

High dimensional feature spaces with relatively few samples usually leads to poor classifier performance for machine learning, neural networks and data mining systems. This paper presents a comparison analysis between correlation-based and causal feature selection for ensemble classifiers. MLP and SVM are used as base classifier and compared with Naive Bayes and Decision Tree. According to the results, correlation-based feature selection algorithm can eliminate more redundant and irrelevant features, provides slightly better accuracy and less complexity than causal feature selection. Ensemble using Bagging algorithm can improve accuracy in both correlation-based and causal feature selection.
机译:具有相对较少样本的高维特征空间通常会导致机器学习,神经网络和数据挖掘系统的分类器性能不佳。本文提出了针对整体分类器的基于相关和因果特征选择的比较分析。 MLP和SVM用作基本分类器,并与朴素贝叶斯和决策树进行比较。根据结果​​,基于相关的特征选择算法可以消除更多的冗余和不相关特征,比因果特征选择提供更高的准确性和更少的复杂性。使用Bagging算法的集成可以提高基于相关性和因果特征选择的准确性。

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