.png){kind=icon}
返回
Feature Interaction Maximisation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2013.04.002.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 plays an important role in classification algorithms. It is particularly useful in dimensionality reduction for selecting features with high discriminative power. This paper introduces a new feature-selection method called Feature Interaction Maximisation (FIM), which employs three-way interaction information as a measure of feature redundancy. It uses a forward greedy search to select features which have maximum interaction information with the features already selected, and which provide maximum relevance. The experiments conducted to verify the performance of the proposed method use three datasets from the UCI repository. The method is compared with four other well-known feature-selection methods: Information Gain (IG), Minimum Redundancy Maximum Relevance (mRMR), Double Input Symmetrical Relevance (DISR), and Interaction Gain Based Feature Selection (IGFS). The average classification accuracy of two classifiers, Naive Bayes and K-nearest neighbour, is used to assess the performance of the new feature-selection method. The results show that FIM outperforms the other methods. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Feature selection
Interaction information
Mutual information
Subset feature selection
Classification
Dimensionality reduction
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:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Particle robotics based on statistical mechanics of loosely coupled components基于松散耦合构件统计力学的粒子机器人技术.png)
Nature
NatureIF0
PREAI
分享收藏Quantitative approach to hückel rule the relations between the cycles of a molecular graph and the thermodynamic stability of a conjugated moleculeTetrahedron
TetrahedronIF0
PREAI
分享收藏
Transition Management for Sustainable Development: A Prescriptive, Complexity‐Based Governance FrameworkGovernance
OAAI
GovernanceIF0
OAAI分享收藏
查看更多内容