An Empirical Evaluation of Techniques for Feature Selection with Cost

Adams, Stephen; Meekins, Ryan; Beling, Peter A.

doi:10.1109/icdmw.2017.153

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…It uses a separating hyperplane to distinguish between the two classes. The problem is limited to minimize the dimensions in the hyperplane, and as a result, feature selection takes place [16].…”

Section: Feature Selection Via Concave Minimization (Fsvfs)mentioning

confidence: 99%

“…The Laplacian method, which is an unsupervised method, evaluates the importance of features by considering that the samples assigned to a particular group are at a shorter distance from each other than the samples in the other group. Thus, the method creates the nearest neighbour graph and ranks the features consistent with the Gauss Laplacian matrix according to their importance [16].…”

Section: Laplacian Score (Ls)mentioning

confidence: 99%

“…UDFS is an unsupervised embedded feature selection method. The method optimizes an objective function representing the minimization problem arranged according to the L2,1 norm, thereby simultaneously making use of discriminatory information and feature correlations [16].…”

Section: Unsupervised Discriminative Feature Selection (Udfs)mentioning

confidence: 99%

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Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Tuncer

2022

Applied Computer Systems

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The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.

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Section: Feature Selection Via Concave Minimization (Fsvfs)mentioning

confidence: 99%

Section: Laplacian Score (Ls)mentioning

confidence: 99%

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Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Tuncer

2022

Applied Computer Systems

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“…CM applications could be characterized by several data sources, leading to large datasets. Although having a lot of data could generate better results; a greater amount of data will result in a higher impact of the curse of dimensionality [36]. Consequently, selecting a subset of relevant features or PVs is crucial to improve the subsequent calculation steps.…”

Section: Introductionmentioning

confidence: 99%

Failure diagnosis of a compressor subjected to surge events: A data-driven framework

Leoni

Carlo

Abaei

et al. 2023

Reliability Engineering & System Safety

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“…Generally, feature selection methods optimize the feature set based on some notion of performance. Costsensitive feature selection allows for the cost of each feature to be incorporated into the feature selection process [2,4,10,20,34,35,38,52].…”

Section: Introductionmentioning

confidence: 99%

ROC with Cost Pareto Frontier Feature Selection Using Search Methods

Meekins

Adams

Farinholt

et al. 2020

Data-Enabled Discov. Appl.

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Cyber-physical systems (CPS) are finding increasing application in many domains. CPS are composed of sensors, actuators, a central decision-making unit, and a network connecting all of these components. The design of CPS involves the selection of these hardware and software components, and this design process could be limited by a cost constraint. This study assumes that the central decision-making unit is a binary classifier, and casts the design problem as a feature selection problem for the binary classifier where each feature has an associated cost. Receiver operating characteristic (ROC) curves are a useful tool for comparing and selecting binary classifiers; however, ROC curves only consider the misclassification cost of the classifier and ignore other costs such as the cost of the features. The authors previously proposed a method called ROC Convex Hull with Cost (ROCCHC) that is used to select ROC optimal classifiers when cost is a factor. ROCCHC extends the widely used ROC Convex Hull (ROCCH) method by combining it with the Pareto analysis for cost optimization. This paper proposes using the ROCCHC analysis as the evaluation function for feature selection search methods without requiring an exhaustive search over the feature space. This analysis is performed on 6 real-world data sets, including a diagnostic cyber-physical system for hydraulic actuators. The ROCCHC analysis is demonstrated using sequential forward and backward search. The results are compared with the ROCCH selection method and a popular Pareto selection method that uses classification accuracy and feature cost.

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An Empirical Evaluation of Techniques for Feature Selection with Cost

Cited by 7 publications

References 17 publications

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Failure diagnosis of a compressor subjected to surge events: A data-driven framework

ROC with Cost Pareto Frontier Feature Selection Using Search Methods

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