Feature selection is the first task of any learning approach to define a relevant set of features. Several methods are proposed to deal with the problem of feature selection including filter, wrapper and embedded methods. In this work, we focus on feature subset selection to select a minimally sized subset of optimal features. Feature Selection is optimization problem; genetic algorithm based attribute subset selection using naïve bayes classifier is used for this purpose. GABASS are found to be the best technique for selection purpose when there is very large population. The GABASS provides good results and their power lies in the good adaptation to the various and fast changing environments.
| Autorius: | Bhupendra Kumar, Somesh Kumar, |
| Leidėjas: | LAP Lambert Academic Publishing |
| Išleidimo metai: | 2016 |
| Knygos puslapių skaičius: | 52 |
| ISBN-10: | 365987163X |
| ISBN-13: | 9783659871634 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Digital and Information technology: general topics |
Parašykite atsiliepimą apie „Attribute Selection with Genetic Algorithm: Genetic Algorithm Based Attributes Subset Selection Using Naive Bays Classifier“
