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A Feature Selection Algorithm Performance Metric for Comparative Analysis
2021
Algorithms
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This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems. ...
The BFI measure can be used to compare the performance of feature selection algorithms across datasets by measuring the change in classifier performance as a result of feature selection, with respect to ...
Conclusions The goal of this study was to propose a novel performance metric: the baseline fitness improvement (BFI) measure for feature selection algorithms, which is unbiased and can be used for comparative ...
doi:10.3390/a14030100
fatcat:h3yen7sb55futo3yuxvspo7c6i
Comparisons of Filter, Wrapper and Embedded-Based Feature Selection Techniques for Consistency of Software Metrics Analysis
2022
SLU Journal of Science and Technology
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techniques as compared with counterpart filter and wrapper-based feature selection techniques ...
Identifying and selecting the most consistent subset of metrics which improves the performance of software defect prediction model is paramount but challenging problem as it receives little attention in ...
The research proposed using Embedded Feature Selection techniques for correlation of a subset of software metrics analysis. ...
doi:10.56471/slujst.v4i.238
fatcat:72wqt2eynzb65dwfo2xqswfc3u
A machine learning approach for feature selection traffic classification using security analysis
2018
Journal of Supercomputing
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2018)A machine learning approach for feature selection traffic classification using security analysis. ...
These metrics select effective features from a traffic flow. However, in order to select robust features from the selected features, we proposed robust features selection algorithm. ...
In the second step (line 13-26), WMI_AUC algorithm selects effective features with AUC metric for a particular ML classifier. ...
doi:10.1007/s11227-018-2263-3
fatcat:tcy2un6yzfdnfcknz3w26ls4fu
Hybrid Optimization Driven RideNN for Software Reusability Estimation
2020
Maǧallaẗ al-abḥāṯ al-handasiyyaẗ
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Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software ...
The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM. ...
Comparative analysis for the Holoentropy = 4 based on (a) MAE, (b) MMRE, and (c) SEM.
Figure 4 . 4 Comparative analysis for the Holoentropy = 5 based on (a) MAE, (b) MMRE, and (c) SEM. ...
doi:10.36909/jer.v8i4.7881
fatcat:3zrymogtkjcqrbmyis4evllvxe
Majority vote feature selection algorithm in software fault prediction
2019
Computer Science and Information Systems
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The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. ...
Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. ...
Another recent work makes use of a hybrid feature selection to improve fault prediction performance of machine learning algorithms [24] . ...
doi:10.2298/csis180312039b
fatcat:cvglwd6xnnacbbmypwy3flw4ja
The ability to classify patients based on gene-expression data varies by algorithm and performance metric
2022
PLoS Computational Biology
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Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. ...
Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies. ...
We thank the Fulton Supercomputing Laboratory at Brigham Young University for providing computational facilities. ...
doi:10.1371/journal.pcbi.1009926
pmid:35275931
pmcid:PMC8942277
fatcat:wg7ryefkg5dalpfbmdodtxgkue
Empirical Study on Filter based Feature Selection Methods for Text Classification
2013
International Journal of Computer Applications
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This paper presents an empirical study comparing performance of few feature selection techniques (Chi-squared, Information Gain, Mutual Information and Symmetrical Uncertainty) employed with different ...
The study further allows comparing the relative performance of the classifiers and the methods. ...
Figure-5 shows the improvement rate of the different classification algorithms using feature selection metrics and figure-6 shows the performance rate of different feature selection metrics. ...
doi:10.5120/14018-2173
fatcat:2wcmeve6yfgmtkxg4tzmxji5tu
Towards a software defect proneness model: feature selection
2021
Applied Aspects of Information Technology
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Feature Selection, Random Forest Importance, LightGBM Importance, Genetic Algorithms, Principal Component Analysis, Xverse python. ...
procedure for predicting software defects based on metric datasets that contain a significant number of highly correlated software code metrics. ...
However, because different projects have been used for research, and software code metrics are widely accepted and widely used in software engineering, the authors believe that the code metrics they have ...
doi:10.15276/aait.04.2021.5
fatcat:khzta3sz3nejtjhe3rk623gwaa
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction
[article]
2021
arXiv
pre-print
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In this work, we study and analyze different feature selection algorithms that can be used to classify cancer subtypes in case of highly varying high-dimensional data. ...
Instead, we apply these algorithms sequentially which helps in lowering the computational cost and improving the predictive performance. ...
In order to understand and compare the effects of common feature selection algorithms for predicting cancer subtypes, we aim to present a comparative study of how the effectiveness of these algorithms ...
arXiv:2109.14648v1
fatcat:dwcx23atnrgq3eqyrltoxk3p4m
Stochastic Embedded Probit Regressive Reweight Boost Classifier for Software Quality Examination
2019
International journal of recent technology and engineering
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The SGNE-PRRBC technique considers the number of program files as input for software quality analysis through feature selection and classification. ...
In software development, Software quality analysis plays a considerable process. Through the software testing, the quality analysis is performed for efficient prediction of defects in the code. ...
The designed technique performs feature selection for software quality analysis. ...
doi:10.35940/ijrte.c1040.1183s319
fatcat:liqdj52u2zb4zgpxsmokxkhuxa
Classification algorithms using multiple MRI features in mild traumatic brain injury
2014
Neurology
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Feature selection was performed using minimal-redundancy maximal-relevance. ...
Conclusion: Multifeature analysis using diffusion-weighted imaging, MFC, fMRI, and volumetrics may aid in the classification of patients with mTBI compared with controls based on optimal feature selection ...
In the machine learning community, it is well known that using multiple features can improve classification performance compared with a single feature alone. ...
doi:10.1212/wnl.0000000000000834
pmid:25171930
pmcid:PMC4180485
fatcat:vklpg6mjubexlgjr3u7mw6ub2m
STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison
[article]
2022
arXiv
pre-print
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STREAMLINE is specifically designed to compare performance between datasets, ML algorithms, and other AutoML tools. ...
' hyperparameter optimization across 15 established algorithms (including less well-known Genetic Programming and rule-based ML), (8) evaluation across 16 classification metrics, (9) model feature importance ...
We also thank the following collaborators for their feedback on application of the pipeline during development: Shannon ...
arXiv:2206.12002v1
fatcat:selkn3u5evhcdlmvxprstxzr4u
ANALYSIS OF SINGLE AND ENSEMBLE MACHINE LEARNING CLASSIFIERS FOR PHISHING ATTACKS DETECTION
2021
International Journal of Software Engineering and Computer Systems
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This study carried out performance analysis of selected single and ensemble machine learning (ML) classifiers in phishing classification.The focus is to investigate how these algorithms behave in the classification ...
Accuracy, Precision, Recall and F1-score were used as performance metrics. Logistic Regression algorithm recorded 0.86 as accuracy, 0.89 as precision, 0.87 as recall and 0.81 as F1-score. ...
ACKNOWLEDGEMENT The authors would like to thank the anonymous reviewers for their comments which helped in improving the article. ...
doi:10.15282/ijsecs.7.2.2021.5.0088
fatcat:24lhlzko5vhxfntg2rwba6ygyi
A Comparative Study of Different Source Code Metrics and Machine Learning Algorithms for Predicting Change Proneness of Object Oriented Systems
[article]
2017
arXiv
pre-print
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Experimental results demonstrate that the model based on selected set of source code metrics after applying feature selection techniques achieves better results as compared to the model using all source ...
In this paper, twenty one source code metrics are computed to develop a statistical model for predicting change-proneness modules. ...
and in the combine basis for change-proneness prediction and select a metric or group of metrics, whomsoever perform better. ...
arXiv:1712.07944v1
fatcat:rknexr7lcraprhiqufkm7cwe24
Artificial Intelligence for Creating Low Latency and Predictive Intrusion Detection with Security Enhancement in Power Systems
2021
Applied Sciences
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In our research we first create a benchmark model for detecting intrusions and then employ various combinations of feature selection techniques based upon ensemble machine learning algorithms to improve ...
The performance of our model was investigated using three evaluation metrics namely: elimination time, accuracy and F1-score. ...
A comparative analysis of feature selection techniques was presented using elimination time as the metric. ...
doi:10.3390/app112411988
fatcat:brvgfr3oibffnjubo5ceea7s3m
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