...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Combinatorial Chemistry & High Throughput Screening >Feature Selection and Classification Employing Hybrid Ant Colony Optimization/Random Forest Methodology
【24h】

Feature Selection and Classification Employing Hybrid Ant Colony Optimization/Random Forest Methodology

机译：混合蚁群优化/随机森林方法的特征选择与分类

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

Accurate classification of instances depends on identification and removal of redundant features. Classification of data having high dimensionality is usually performed in conjunction with an appropriate feature selection method. Feature selection enables identification of the most informative feature subset from the enormously vast search space that can accurately classify the given data. We propose an ant colony optimization (ACO)/random forest based hybrid filterwrapper search technique, which traverses the search space and selects a feature subset with high classifying ability. We evaluate the performance of our algorithm on four widely studied CoEPrA (Comparative Evaluation of Prediction Algorithms, http://coepra.org) datasets. The performance of the software ants mediated hybrid filter/wrapper approach compares well with the available competition results. Thus, the proposed Ant Colony Optimization based technique can effectively find small feature subsets capable of classifying with a very good accuracy and can be employed for feature subset selection with a high level of confidence.
机译：实例的准确分类取决于冗余特征的识别和删除。通常结合适当的特征选择方法对具有高维度的数据进行分类。通过特征选择，可以从可以准确地对给定数据进行分类的巨大搜索空间中识别出最具信息量的特征子集。我们提出了一种基于蚁群优化（ACO）/随机森林的混合filterwrapper搜索技术，该技术遍历搜索空间并选择具有高分类能力的特征子集。我们在四个广泛研究的CoEPrA（预测算法的比较评估，http：//coepra.org）数据集上评估了我们算法的性能。软件蚂蚁介导的混合过滤器/包装器方法的性能与可用的竞争结果很好地比较。因此，所提出的基于蚁群优化的技术可以有效地找到能够以非常好的准确性进行分类的小特征子集，并且可以以高置信度用于特征子集选择。

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