L1 Penalized Regression Procedures for Feature Selection
نویسندگان
چکیده
منابع مشابه
Feature Selection via l1-Penalized Squared-Loss Mutual Information
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose l1-LSMI, an l1...
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ژورنال
عنوان ژورنال: International Journal of Scientific Research in Mathematical and Statistical Sciences
سال: 2018
ISSN: 2348-4519
DOI: 10.26438/ijsrmss/v5i5.8891