چکیده مقاله
Today, Feature Selection, as a technique to improve classification methods, has attracted the attention of many computer scientists As the methods of extracting data is improving every day, we encounter matrices with huge dimension and it will affect the performance of processing Hence, reducing the number of features by choosing the best subset of all features, will affect the algorithms performance Finding the best subset by comparing all possible subsets, is an intractable process, as a solution, heuristic methods can help to find the near optimal solutions with an acceptable complexity In this paper, we introduce a novel Feature Selection technique which selects the most informative features and omits the redundant or irrelevant ones Our method is embedded in CS Cuckoo Search The performance of our method is evaluated on two classification benchmarks: Vowel, and Wine Comparing the results with four state of the art methods, demonstrates its superiority over them
کلیدواژهها
نویسندگان
Z. Shojaee
Department of Computer Science, Yazd University, Yazd, Iran
S.A Shahzadeh Fazeli
Department of Computer Science, Yazd University, Yazd, Iran
E. Abbasi
Department of Computer Science, Yazd University, Yazd, Iran
F. Adibnia
Department of Computer Engineering, Yazd University, Yazd, Iran
شیوه ارجاع
Shojaee, Z. and Shahzadeh Fazeli, S.A and Abbasi, E. and Adibnia, F.,1398,A Cuckoo Search Optimization based Method for Feature Selection,3rd International Conference on Soft Computing,Rudsar
ارائهشده در
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