کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8953859 1645963 2018 39 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach
ترجمه فارسی عنوان
مجموعه ای از ویژگی های کوچک مجموعه بزرگ بزرگ: یک رویکرد اکتشافی ترکیبی با الهام از زیست محیطی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی
Selection of a representative set of features is still a crucial and challenging problem in machine learning. The complexity of the problem increases when any of the following situations occur: a very large number of attributes (large dimensionality); a very small number of instances or time points (small-instance set). The first situation poses problems for machine learning algorithm as the search space for selecting a combination of relevant features becomes impossible to explore in a reasonable time and with reasonable computational resources. The second aspect poses the problem of having insufficient data to learn from (insufficient examples). In this work, we approach both these issues at the same time. The methods we proposed are heuristics inspired by nature (in particular, by biology). We propose a hybrid of two methods which has the advantage of providing a good learning from fewer examples and a fair selection of features from a really large set, all these while ensuring a high standard classification accuracy of the data. The methods used are antlion optimization (ALO), grey wolf optimization (GWO), and a combination of the two (ALO-GWO). We test their performance on datasets having almost 50,000 features and less than 200 instances. The results look promising while compared with other methods such as genetic algorithms (GA) and particle swarm optimization (PSO).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Swarm and Evolutionary Computation - Volume 42, October 2018, Pages 29-42
نویسندگان
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