کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4943070 1437623 2017 54 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel bacterial foraging optimization algorithm for feature selection
ترجمه فارسی عنوان
یک الگوریتم بهینه سازی نگهداری باکتریایی برای انتخاب ویژگی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
In this study, ACBFO and ISEDBFO are tested with 10 public data sets of UCI. The performance of the proposed methods is compared with particle swarm optimization based, genetic algorithm based, simulated annealing based, ant lion optimization based, binary bat algorithm based and cuckoo search based approaches. The experimental results demonstrate that the average classification accuracy of the proposed algorithms is nearly 3 percentage points higher than other tested methods. Furthermore, the improved algorithms reduce the length of the feature subset by almost 3 in comparison to other methods. In addition, the modified methods achieve excellent performance on wilcoxon signed-rank test and sensitivity-specificity test. In conclusion, the novel BFO algorithms can provide important support for the expert and intelligent systems.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 83, 15 October 2017, Pages 1-17
نویسندگان
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