Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Thejas, G. S.; Garg, Rameshwar; Iyengar, S. S.; Sunitha, N. R.; Badrinath, Prajwal; Chennupati, Shasank

doi:10.1109/access.2021.3112169

Cited by 22 publications

(6 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genetic Algorithms (GAs) 17 – 19 use principles of natural evolution to explore the feature space. GAs is particularly useful for high-dimensional datasets but are computationally expensive and can suffer from premature convergence levels 20 – 22 . PSO simulates the social behavior of birds and fish to explore the feature space.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

View full text Add to dashboard Cite

Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

show abstract

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The mutual information value is normalized to [0, 1] to obtain the NMI value N M I(X; Y ), see equation (6).…”

Section: Nmi-based Redundant Feature Filteringmentioning

confidence: 99%

“…The wrapper method [2,3] is mainly based on the training effect of the subsequent machine learning algorithms for the feature selection, which requires many times of training and has a large computational cost. The embedding method [4][5][6] integrates feature selection and model training into one process and achieves feature selection while training, but its parameter setting is complicated and the time complexity is high. Typhoon related data is a kind of spatio-temporal series data.…”

mentioning

confidence: 99%

A parallel feature selection method based on NMI-XGBoost and distance correlation for typhoon trajectory prediction

Qiao,

Wu,

Wang

et al. 2024

J Supercomput

View full text Add to dashboard Cite

Typhoon trajectory related data involves many factors such as atmospheric factors, oceanic factors, and physical factors. It has the characteristics of high dimension, strong spatio-temporal correlation, and non-linear correlation, which increases the difficulty of typhoon trajectory prediction. Using feature selection approaches to select appropriate prediction factors becomes an important means to reduce the dimension of typhoon trajectory related data and improve the performance and accuracy of typhoon trajectory prediction methods. However, the existing feature selection methods based on linear correlation analysis can not well depict the nonlinear correlation between data features, which results in low accuracy of feature selection. The feature selection methods based on nonlinear correlation analysis are computationally expensive, which affects the timeliness of feature selection. To solve the problem, we propose a parallel feature selection method NX-Spark-DC based on the Spark platform for typhoon trajectory related data. The method firstly filters out the redundant Article Title features of typhoon related data by Normalized Mutual Information (NMI) method, subsequently eliminates the useless features by XGBoost machine learning model, and thus reducing the dimension of typhoon related data. On this basis, an improved Spark-based parallel distance correlation algorithm (Spark-DC) is proposed to select the feature combinations with strong correlation. A series of experimental results show that NX-Spark-DC method has high execution efficiency and accuracy, which is significantly better than the existing methods.

show abstract

“…Elimination of in-significant features results in dimensionality reduction. Filter, wrapper, and hybrid methods are conventional feature selection methods [14]. In the high-dimension dataset, certain features have a low correlation with the response variable of the dataset.…”

Section: Feature Selectionmentioning

confidence: 99%

Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

Ziyad,

Aljohani,

Saeed

2023

CMC

View full text Add to dashboard Cite

Autism spectrum disorder (ASD), classified as a developmental disability, is now more common in children than ever. A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children. Parents can seek professional help for a better prognosis of the child's therapy when ASD is diagnosed under five years. This research study aims to develop an automated tool for diagnosing autism in children. The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition, feature selection, and classification phases. The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification. The Imperialistic competitive algorithm (ICA) based on empires conquering colonies performs feature selection in this study. The performance of Logistic Regression (LR), Decision tree, K-Nearest Neighbor (KNN), and Random Forest (RF) classifiers are experimentally studied in this research work. The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset. The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection method and different classifiers. The Exploratory Data Analysis (EDA) phase has uncovered crucial facts about the data, like the correlation of the features in the dataset with the class variable.

show abstract

Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Cited by 22 publications

References 68 publications

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

A parallel feature selection method based on NMI-XGBoost and distance correlation for typhoon trajectory prediction

Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

Contact Info

Product

Resources

About