Research on Feature Selection Methods based on Random Forest
نویسندگان
چکیده
منابع مشابه
A Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis (RESEARCH NOTE)
Machine learning-based classification techniques provide support for the decision making process in the field of healthcare, especially in disease diagnosis, prognosis and screening. Healthcare datasets are voluminous in nature and their high dimensionality problem comprises in terms of slower learning rate and higher computational cost. Feature selection is expected to deal with the high dimen...
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Disease risk prediction is an important task in biomedicine and bioinformatics. To resolve the problem of high-dimensional features space and highly feature redundancy and to improve the intelligibility of data mining results, a new wrapper method of feature selection based on random forest variables importance measures and support vector machine was proposed. The proposed method combined seque...
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Feature selection has become one of the most active research areas in the field of data mining. It allows removing redundant and irrelevant data sets of large size. Furthermore, there are several methods in the literature for selecting attributes. In this article, a new multi-objective method is proposed to select relevant and non-redundant features. Our proposed feature selection method is div...
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Feature selection and data sampling are two of the most important data preprocessing activities in the practice of data mining. Feature selection is used to remove less important features from the training data set, while data sampling is an effective means for dealing with the class imbalance problem. While the impacts of feature selection and class imbalance have been frequently investigated ...
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ژورنال
عنوان ژورنال: Tehnicki Vjesnik-technical Gazette
سال: 2023
ISSN: ['1330-3651', '1848-6339']
DOI: https://doi.org/10.17559/tv-20220823104912