Forming an optimal feature set for classifying network intrusions involving multiple feature selection methods

2010 ◽  
Author(s):  
Kok-Chin Khor ◽  
Choo-Yee Ting ◽  
Somnuk-Phon Amnuaisuk
Keyword(s):  
Feature Selection ◽  
Selection Methods ◽  
Multiple Feature ◽  
Network Intrusions ◽  
Optimal Feature

scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

scholarly journals Multi-label Feature Selection via Global Relevance and Redundancy Optimization

2020 ◽  
Author(s):  
Jia Zhang ◽  
Yidong Lin ◽  
Min Jiang ◽  
Shaozi Li ◽  
Yong Tang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Empirical Studies ◽  
Data Sets ◽  
Selection Methods ◽  
Label Data ◽  
Optimization Framework ◽  
Feature Relevance ◽  
Optimal Feature Selection ◽  
Effectiveness And Efficiency ◽  
Optimal Feature

Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.

scholarly journals Ensemble Classification Based on Feature Selection for Environmental Sound Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Shuai Zhao ◽  
Yan Zhang ◽  
Haifeng Xu ◽  
Te Han
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Ensemble Classification ◽  
Sound Recognition ◽  
Selection Methods ◽  
Improved Method ◽  
Environmental Sound ◽  
Selection For ◽  
Optimal Feature ◽  
Environmental Sound Recognition

Environmental sound recognition has been a hot topic in the domain of audio recognition. How to select the optimal feature subsets and enhance the performance of classification precisely is an urgent problem to be solved. Ensemble learning, a new kind of method presented recently, has been an effective way to improve the accuracy of classification in feature selection. In this paper, experiments were performed on environmental sound dataset. An improved method based on constraint score and multimodels ensemble feature selection methods (MmEnFs) were exploited in the experiments. The experimental results show that when enough attributes are selected, the improved method can get a better performance compared to other feature selection methods. And the ensemble feature selection method, which combines other methods, can obtain the optimal performance in most cases.

Feature distillation and accumulated selection for automated fraudulent publisher classification from user click data of online advertising

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepti Sisodia ◽  
Dilip Singh Sisodia
Keyword(s):  
Feature Selection ◽  
Online Advertising ◽  
Feature Selection Method ◽  
Majority Voting ◽  
Feature Subset ◽  
Relevant Feature ◽  
Selection Methods ◽  
Content Type ◽  
Optimal Feature Subset ◽  
Optimal Feature

PurposeThe problem of choosing the utmost useful features from hundreds of features from time-series user click data arises in online advertising toward fraudulent publisher's classification. Selecting feature subsets is a key issue in such classification tasks. Practically, the use of filter approaches is common; however, they neglect the correlations amid features. Conversely, wrapper approaches could not be applied due to their complexities. Moreover, in particular, existing feature selection methods could not handle such data, which is one of the major causes of instability of feature selection.Design/methodology/approachTo overcome such issues, a majority voting-based hybrid feature selection method, namely feature distillation and accumulated selection (FDAS), is proposed to investigate the optimal subset of relevant features for analyzing the publisher's fraudulent conduct. FDAS works in two phases: (1) feature distillation, where significant features from standard filter and wrapper feature selection methods are obtained using majority voting; (2) accumulated selection, where we enumerated an accumulated evaluation of relevant feature subset to search for an optimal feature subset using effective machine learning (ML) models.FindingsEmpirical results prove enhanced classification performance with proposed features in average precision, recall, f1-score and AUC in publisher identification and classification.Originality/valueThe FDAS is evaluated on FDMA2012 user-click data and nine other benchmark datasets to gauge its generalizing characteristics, first, considering original features, second, with relevant feature subsets selected by feature selection (FS) methods, third, with optimal feature subset obtained by the proposed approach. ANOVA significance test is conducted to demonstrate significant differences between independent features.

COMBINATION OF MULTIPLE FEATURE SELECTION METHODS FOR TEXT CATEGORIZATION BY USING COMBINATORIAL FUSION ANALYSIS AND RANK-SCORE CHARACTERISTIC

2013 ◽  
Vol 22 (02) ◽  
pp. 1350001 ◽  
Author(s):  
YANJUN LI ◽  
D. FRANK HSU ◽  
SOON M. CHUNG
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Score Function ◽  
Individual Feature ◽  
Selection Methods ◽  
Multiple Feature ◽  
Rank Score ◽  
Combinatorial Fusion ◽  
Fusion Analysis

Effective feature selection methods are important for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. Extensive research has been done to improve the performance of individual feature selection methods. However, it is always a challenge to come up with an individual feature selection method which would outperform other methods in most cases. In this paper, we explore the possibility of improving the overall performance by combining multiple individual feature selection methods. In particular, we propose a method of combining multiple feature selection methods by using an information fusion paradigm, called Combinatorial Fusion Analysis (CFA). A rank-score function and its associated graph, called rank-score graph, are adopted to measure the diversity of different feature selection methods. Our experimental results demonstrated that a combination of multiple feature selection methods can outperform a single method only if each individual feature selection method has unique scoring behavior and relatively high performance. Moreover, it is shown that the rank-score function and rank-score graph are useful for the selection of a combination of feature selection methods.

scholarly journals A Bootstrap Framework for Aggregating within and between Feature Selection Methods

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 200
Author(s):  
Reem Salman ◽  
Ayman Alzaatreh ◽  
Hana Sulieman ◽  
Shaimaa Faisal
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Feature Selection Method ◽  
Selection Methods ◽  
Aggregation Method ◽  
Single Feature ◽  
Multiple Feature ◽  
Multiple Domains ◽  
And Control ◽  
Feature Selection Techniques

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.

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