.png){kind=icon}
返回
Wrapper Framework for Test-Cost-Sensitive Feature Selection
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2019.2904662.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Feature selection is an optional preprocessing procedure and is frequently used to improve the classification accuracy of a machine learning algorithm by removing irrelevant and/or redundant features. However, in many real-world applications, the test cost is also required for making optimal decisions, in addition to the classification accuracy. To the best of our knowledge, thus far, few studies have been conducted on test-cost-sensitive feature selection (TCSFS). In TCSFS, the objectives are twofold: 1) to improve the classification accuracy and 2) to decrease the test cost. Therefore, in fact, it constitutes a multiobjective optimization problem. In this paper, we transformed this multiobjective optimization problem into a single-objective optimization problem by utilizing a new evaluation function and in this paper, we propose a new general wrapper framework for TCSFS. Specifically, in our proposed framework, we add a new term to the evaluation function of a wrapper feature selection method so that the test cost of measuring features is taken into account. We experimentally tested our proposed framework, using 36 classification problems from the University of California at Irvine (UCI) repository, and compared it to some other state-of-the-art feature selection frameworks. The experimental results showed that our framework allows users to select an optimal feature subset with the minimal test cost, while simultaneously maintaining a high classification accuracy.
Keyword:
Feature extraction
Optimization
Support vector machines
Geology
Training
Medical diagnosis
Data mining
Classification accuracy
decision making
feature selection
test cost
test-cost-sensitive learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
An Information Theory-Based Feature Selection Framework for Big Data Under Apache SparkApache Spark下基于信息论的大数据特征选择框架
PREAI
分享收藏分享收藏A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm一种简单快速的基于超体积指标的多目标进化算法
PREAI
分享收藏
查看更多内容