چکیده مقاله
A recently encountered challenge in data science and more specifically in machine learning is growing amount of data Exclusion of superfluous data and thereby focusing on essential variables, known as feature selection, proves vital for model performance optimization This study undertakes a thorough investigation into pivotal feature selection strategies The benefits and limitations of each method is clearly stated An advanced methodology is also presented for tackling incomplete datasets, alongside introducing an innovative hybrid model that unites the Partial Mutual Information Criterion PMIC , state of the art null value completion strategies and neural network synergies to improve feature selection processes Moreover, a newly defined benchmark, rational success weight is defined to certify the efficiency of our proposed algorithm in selecting relevant features Finally, the suggested strategy is implemented in Python and numerical test results are reported on randomly generated data sets
کلیدواژهها
نویسندگان
Behrang Ebrahimi
Department of Engineering Sciences, University of Tehran, Tehran, Iran
Negin Bagherpour
Department of Engineering Sciences, University of Tehran, Tehran, Iran
شیوه ارجاع
Ebrahimi, Behrang and Bagherpour, Negin,1402,A Weighted Approach for Feature Selection in High-Dimensional and Incomplete Data,5th International Conference on Software Computing,Rudsar
ارائهشده در
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