Online group streaming feature selection considering feature interaction

2021 ◽  
pp. 107157
Author(s):  
Peng Zhou ◽  
Ni Wang ◽  
Shu Zhao
Keyword(s):  
Feature Selection ◽  
Feature Interaction ◽  
Online Group

scholarly journals A Mixed Feature Selection Method Considering Interaction

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zilin Zeng ◽  
Hongjun Zhang ◽  
Rui Zhang ◽  
Youliang Zhang
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Feature Selection Method ◽  
Feature Space ◽  
Feature Interaction ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Neighborhood Rough Sets ◽  
Real World Datasets ◽  
Neighborhood Interaction

Feature interaction has gained considerable attention recently. However, many feature selection methods considering interaction are only designed for categorical features. This paper proposes a mixed feature selection algorithm based on neighborhood rough sets that can be used to search for interacting features. In this paper, feature relevance, feature redundancy, and feature interaction are defined in the framework of neighborhood rough sets, the neighborhood interaction weight factor reflecting whether a feature is redundant or interactive is proposed, and a neighborhood interaction weight based feature selection algorithm (NIWFS) is brought forward. To evaluate the performance of the proposed algorithm, we compare NIWFS with other three feature selection algorithms, including INTERACT, NRS, and NMI, in terms of the classification accuracies and the number of selected features with C4.5 and IB1. The results from ten real world datasets indicate that NIWFS not only deals with mixed datasets directly, but also reduces the dimensionality of feature space with the highest average accuracies.

scholarly journals Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Real Life ◽  
Classification Model ◽  
Cluster Method ◽  
Feature Interaction ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Multilabel Classification ◽  
Overlapping Clustering

Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC. Typically, existing cluster-based MLC frameworks employ a hard cluster method. In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class. Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm. Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable.

A novel hybrid feature selection method considering feature interaction in neighborhood rough set

2021 ◽  
pp. 107167
Author(s):  
Jihong Wan ◽  
Hongmei Chen ◽  
Zhong Yuan ◽  
Tianrui Li ◽  
Xiaoling Yang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Interaction ◽  
Neighborhood Rough Set

Online Streaming Feature Selection Based on Feature Interaction

2020 ◽  
Author(s):  
Yan Lv ◽  
Yaojin Lin ◽  
Xiangyan Chen ◽  
Dongxing Wang ◽  
Chenxi Wang
Keyword(s):  
Feature Selection ◽  
Feature Interaction ◽  
Online Streaming

scholarly journals Feature Selection with Conditional Mutual Information Considering Feature Interaction

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 858 ◽  
Author(s):  
Jun Liang ◽  
Liang Hou ◽  
Zhenhua Luan ◽  
Weiping Huang
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Classification Performance ◽  
Feature Interaction ◽  
Conditional Mutual Information ◽  
Selection Algorithm ◽  
Benchmark Datasets ◽  
Feature Relevance ◽  
New Feature ◽  
Selection Algorithms

Feature interaction is a newly proposed feature relevance relationship, but the unintentional removal of interactive features can result in poor classification performance for this relationship. However, traditional feature selection algorithms mainly focus on detecting relevant and redundant features while interactive features are usually ignored. To deal with this problem, feature relevance, feature redundancy and feature interaction are redefined based on information theory. Then a new feature selection algorithm named CMIFSI (Conditional Mutual Information based Feature Selection considering Interaction) is proposed in this paper, which makes use of conditional mutual information to estimate feature redundancy and interaction, respectively. To verify the effectiveness of our algorithm, empirical experiments are conducted to compare it with other several representative feature selection algorithms. The results on both synthetic and benchmark datasets indicate that our algorithm achieves better results than other methods in most cases. Further, it highlights the necessity of dealing with feature interaction.

A novel feature selection method considering feature interaction

2015 ◽  
Vol 48 (8) ◽  
pp. 2656-2666 ◽  
Author(s):  
Zilin Zeng ◽  
Hongjun Zhang ◽  
Rui Zhang ◽  
Chengxiang Yin
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Feature Interaction

Feature interaction based online streaming feature selection via buffer mechanism

2021 ◽  
Author(s):  
Yan Lv ◽  
Yaojin Lin ◽  
Xiangyan Chen ◽  
Chenxi Wang ◽  
Shaozi Li
Keyword(s):  
Feature Selection ◽  
Feature Interaction ◽  
Online Streaming
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