Feature selection is an important part of machine learning. The Feature selection refers to the process of reducing the inputs for processing and analysis, or of finding the most meaningful inputs. A related term, feature engineering (or feature extraction), refers to the process of extracting useful information or features from existing data. Mining of particular information related to a concept is done on the basis of the feature of the data.
The accessing of these features hence for data retrieval can be termed as the feature extraction mechanism. Different type of feature extraction methods is being used. In this paper, the different feature selection methodologies are examined in terms of need and method adopted for feature selection.
The three types of method are mainly available, such as Shannon's Entropy, Bayesian with K2 Prior and Bayesian Dirichlet with uniform prior (default). The objectives of this survey paper is to identify the existing contribution made by using their above mentioned algorithms and the result obtained.
by R. Pradeepa | K. Palanivel"A Survey on Classification of Feature Selection Strategies"
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018,
URL: http://www.ijtsrd.com/papers/ijtsrd9632.pdf
Direct Link - http://www.ijtsrd.com/computer-science/data-miining/9632/a-survey-on-classification-of-feature-selection-strategies/r-pradeepa
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