Feature Selection Using Neighborhood Positive Approximation Rough Set

Feature Selection Using Neighborhood Positive Approximation Rough Set

Mohammad Atique (Sant Gadge Baba Amravati University, India) and Leena Homraj Patil (Priyadarshini Institute of Engineering, India)
Copyright: © 2018 |Pages: 26
DOI: 10.4018/978-1-5225-5775-3.ch005
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Abstract

Attribute reduction and feature selection is the main issue in rough set. Researchers have focused on several attribute reduction using rough set. However, the methods found are time consuming for large data sets. Since the key lies in reducing the attributes and selecting the relevant features, the main aim is to reduce the dimensionality of huge amount of data to get the smaller subset which can provide the useful information. Feature selection approach reduces the dimensionality of feature space and improves the overall performance. The challenge in feature selection is to deal with high dimensional. To overcome the issues and challenges, this chapter describes a feature selection based on the proposed neighborhood positive approximation approach and attributes reduction for data sets. This proposed system implements for attribute reduction and finds the relevant features. Evaluation shows that the proposed neighborhood positive approximation algorithm is effective and feasible for large data sets and also reduces the feature space.
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Feature selection, known as attribute reduction has turn out to be a significant step. In rough set theory, features selection aims to maintain the discriminatory power of original features. Hence, it is therefore important to reduce dimensionality of the data to smaller set of features and relevant information for decreasing the cost in storing and reduction in the processing time. To overcome the issue of feature selection and attribute reduction, few attributes can be omitted, which will not seriously effect on classification accuracy (Patil & Atique, 2015). Rough set theory handles data sets with imprecision and uncertain information. It utilizes a study of attribute reduction information system. It achieves concept approximation from the universe through which two defined subsets are produced. So far, many researchers have studied an attribute reduction algorithm.

Pawlak’s Rough Set Model

Rough set was initially instigated by Pawlak as a efficient approach which deals with uncertainty. It works on decision analysis, knowledge discovery, and conflict analysis. The two-defined subset lower and upper are obtained through concept approximation. These two operators define an equivalence relation (Pawlak, 1991). Rough set utilizes the similarity to partition space data and create jointly equivalence class as the essential concepts. It's applicable only to data with small attributes.

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