Improving the Machine Learning Prediction Accuracy with Clustering Discretization

Gao, Chunlan; Zhang, Yanqing; Lo, Dan; Shi, Yong; Huang, Jian

doi:10.1109/ccwc54503.2022.9720805

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Studies by Gao et al [12] on clustering-based discretization shows substantial improvement in logistic regression accuracy for predicting heart-related deaths. Elhilbawi et al [13] investigate the effects of discretization methods like MDLP, class-attribute interdependence maximization (CAIM), and ID3 on mortality prediction in intensive care unit (ICU), demonstrating significant enhancements in classification accuracy with support vector machine (SVM), random forest, and KNN models.…”

Section: Preliminaries 21 Discretization Overviewmentioning

confidence: 99%

An improve unsupervised discretization using optimization algorithms for classification problems

Mohamed,

Samsudin

2024

IJEECS

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This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. Recognizing the critical role of discretization in enhancing classification performance, the study integrates equal width binning (EWB) with two optimization algorithms: the bat algorithm (BA), referred to as EB, and the whale optimization algorithm (WOA), denoted as EW. The primary objective is to determine the optimal technique for predicting relevant class labels. The paper emphasizes the significance of discretization in data preprocessing, offering a comprehensive approach that combines discretization techniques with optimization algorithms. An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. According to the experimental findings, the suggested EB has a major effect on the accuracy, recall, and F-measure of data classification. The classification performance using EB outperforms other existing approaches for all datasets.

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Section: Preliminaries 21 Discretization Overviewmentioning

confidence: 99%

An improve unsupervised discretization using optimization algorithms for classification problems

Mohamed,

Samsudin

2024

IJEECS

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“…When F ( d i , q i , b i , X ) is 1, F ( x i , y i , z i ) can be expressed by the adjustment factor distribution function Cauthy (0, 1). When F ( d i , q i , b i , X ) is 0, the external uncertainty of physical exercise is the highest; otherwise, the uncertainty is the lowest Gao et al [ 14 ]. Because the two sides of Cauthy (0, 1) tend to an extreme value slowly, its distribution speed is less than Gauss (0, 1), thus reducing the uncertainty of physical exercise.…”

Section: Concepts Related To the Continuous Discrete Algorithmmentioning

confidence: 99%

Analysis of Influencing Factors of College Students’ Physical Exercise Habits Based on the Continuous Discrete Algorithm

Zhang

Jiang

Shi

et al. 2022

Journal of Environmental and Public Health

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At present, college students give great importance to their physical training, but their physical habits are poor and they cannot exercise regularly. In view of the influencing factors in the habit formation of physical exercise, this paper puts forward a continuous discrete algorithm to provide students with a scientific and reasonable habit formation scheme. First, the data of college students’ physical exercise habits are collected, and the data are cleaned and continuously analyzed to initially form a habit formation data set; Then, the information gain and clustering thought in the discrete algorithm are used to judge the data change, and the ordered relationship among the factors is obtained. Finally, in judging the influence degree of each factor, we find out the final influencing factors. MATLAB simulation shows that the continuous discrete algorithm can accurately analyze the problem of physical exercise habit formation and sort the influencing factors, with an accuracy rate of 90% and a calculation time of less than 19 minutes, which is significantly better than the original discrete algorithm.

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Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty

Chavosh Nejad,

Vestergaard Matthiesen,

Dukovska-Popovska

et al. 2024

International Journal of Medical Informatics

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Improving the Machine Learning Prediction Accuracy with Clustering Discretization

Cited by 4 publications

References 6 publications

An improve unsupervised discretization using optimization algorithms for classification problems

An improve unsupervised discretization using optimization algorithms for classification problems

Analysis of Influencing Factors of College Students’ Physical Exercise Habits Based on the Continuous Discrete Algorithm

Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty

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