Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

• URL: http://arxiv.org/abs/2407.15611v1
• Date: Mon, 22 Jul 2024 13:08:50 GMT
• Title: Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets
• Authors: Hossein Nematzadeh, Joseph Mani, Zahra Nematzadeh, Ebrahim Akbari, Radziah Mohamad,
• Abstract summary: There isn't a universally optimal feature selection method applicable to any data distribution. This paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works.
• Score: 2.6037922505725675
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Feature selection poses a challenge in small-sample high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There isn't a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response, this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. The implementation and corresponding data are available at https://github.com/hnematzadeh/DMC-GAwAR

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