A review of feature selection methods on synthetic data

Bolón-Canedo, Verónica; Sánchez-Maroño, Noelia; Alonso‐Betanzos, Amparo

doi:10.1007/s10115-012-0487-8

Cited by 646 publications

(403 citation statements)

References 46 publications

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“…This is a feedback method based on two components: search and evaluation. The search component generates parameter settings that are then evaluated using the evaluation component [16] . It uses a learning algorithm to evaluate the usefulness of features and thus produces better feature subsets.…”

Section: Wrapper Methodsmentioning

confidence: 99%

Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

Balasaraswathi

Sugumaran

Hamid

2017

J. Commun. Inf. Netw.

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As Internet access widens, IDS (Intrusion Detection System) is becoming a very important component of network security to prevent unauthorized use and misuse of data. An IDS routinely handles massive amounts of data traffic that contain redundant and irrelevant features, which impact the performance of the IDS negatively. Feature selection methods play an important role in eliminating unrelated and redundant features in IDS. Statistical analysis, neural networks, machine learning, data mining techniques, and support vector machine models are employed in some such methods. Good feature selection leads to better classification accuracy. Recently, bio-inspired optimization algorithms have been used for feature selection. This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms.

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Section: Wrapper Methodsmentioning

confidence: 99%

Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

Balasaraswathi

Sugumaran

Hamid

2017

J. Commun. Inf. Netw.

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“…There are mainly two sorts of feature selection methods, "wrapper" and "filter" methods [14]. Wrapper methods search for feature subsets of bug reports and find the subset with the highest quality [15].…”

Section: Feature Selection For Bug Report Prioritizationmentioning

confidence: 99%

“…Filter methods score for each feature of bug reports based on some criteria, independently. Then the features are ranked according to the score and top N features are selected [14]. In this paper 7 most popular feature selection methods CfsSubset (CFS), Correlation (CO), GainRatio (GR), InfoGain (IG), OneR (OR), ReliefF (RF) and SymmetricalUncert (SU) are studied.…”

Section: Feature Selection For Bug Report Prioritizationmentioning

confidence: 99%

Exploring the Influence of Feature Selection Techniques on Bug Report Prioritization

Wang¹,

Zhang

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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To improve software quality, developers often open a bug repository and allow users to find bugs, describe bugs in the form of bug reports and submit bug reports to the repository. Based on the description, testers assign a priority to each bug report. In the beginning the process of priority assignment is performed manually. With the increasing amount of bug reports, researchers introduced classification methods to assign priorities automatically with all the features considered. In this paper feature selection methods are introduced to improve the effect of bug report prioritization using classification models. The experimental results show that feature selection based on Information Gain and Pearson Correlation can improve the precision and recall for bug report prioritization on two models, i.e., SVM and Naive Bayes.

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“…ReliefF is an extension of the original Relief algorithm [25]. Previous research has shown that it has good performance regardless of the specific data [5]. IG is an information theoretic method that measures the information gain of the class variable when the attribute variable is given.…”

Section: Experimental Settingmentioning

confidence: 99%

A new transferred feature selection algorithm for customer identification

Zhu¹,

Niu²,

Xiao³

et al. 2016

Neural Comput & Applic

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Class imbalance brings great challenges to feature selection in customer identification and most of current feature selection approaches cannot produce good prediction on the minority class. A number of studies have attempted to solve this issue by using resampling techniques. However, resampling techniques only use the in-domain information and they cannot achieve good performance when the imbalance is caused by the absolute rarity of the minority class. In this paper, we focus on the issue of feature selection with class imbalance caused by absolute rarity. By introducing the idea of transfer learning, we develop a transferred feature selection method based on the group method of data handling neural network (GMDH). The proposed ensemble neural network extracts information of similar customers from related domains to deal with the information scarcity of the minority class in the target domain. Experiments are done on a real-world application from cigarette company. The results indicate that the new method gives better predictive performance than other benchmark feature selection methods, especially the predictive accuracy of minority high-value customers. At the same time, the new algorithm can help to identify important features that distinguish highvalue customers from low-value ones.

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A review of feature selection methods on synthetic data

Cited by 646 publications

References 46 publications

Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

Exploring the Influence of Feature Selection Techniques on Bug Report Prioritization

A new transferred feature selection algorithm for customer identification

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