Understanding and using rough set based feature selection : concepts, techniques and applications /: concepts, techniques and applications. ([2019])

• Record Type: Book
• Further Information: Note: Muhammad Summair Raza, Usman Qamar.
• Authors: Raza, Muhammad Summair; Qamar, Usman
• Contents: Chapter-1: Introduction to Feature Selection This chapter will discuss feature selection, its background, advantages and practical applications. Chapter-2: Background This chapter will explain various Non-RST based Feature Selection approaches from literature along with strengths and weaknesses of each. Chapter-3: Rough Set Theory This chapter will provide introduction of Rough Set theory along with its background, particular features and differences from other set theories. As well as discuss basic concepts of rough Set theory. Examples will also be provided by using very small sample datasets. Chapter-4: Advance Concepts in Rough Set theory This chapter will discuss some advance concepts like rough set based heuristics, rules, lemmas etc. Chapter-5: Rough Set Theory Based Feature Selection Techniques Rough set theory has been successfully used for feature selection techniques. In this chapter, we will present various feature selection techniques which use RST concepts. Chapter-6: Unsupervised Feature Selection Using RST Unsupervised feature selection information that could find feature subsets without given any class labels. In this section, we will discuss some of the unsupervised feature subset algorithms based on rough set theory. Chapter-7: Critical Analysis of Feature Selection Algorithms Critical review of each approach discussed. Critical review will include strengths and weaknesses of each. Special emphasis will be given on complexity analysis of each approach. Chapter -8: Dominance based Rough Set Approach Dominance-based rough set approach (DRSA) is an extension to the conventional rough set approach which supports the preference order using dominance principle where an item having higher value of attributes should belong to higher decision classes. Chapter -9: Fuzzy-Rough Sets Fuzzy rough sets were introduced as a fuzzy generalization of rough sets. In this chapter, we discuss general approach to the fuzzification of rough sets. Chapter-10: Introduction to Classic Rough Set Based APIs library This chapter will provide details explanation of the RST based API library (that will provided with the book) along with working example of each of the API function. This chapter will work as instruction manual for the library. Chapter-11: Dominance Based Rough Set API library This chapter will provide details explanation of the dominance based RST API along with working example of each of the API function.
• Edition: Second edition
• Publisher Details: Singapore : Springer
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource, illustrations (some color)
• Subjects: 511.3/22; Rough sets; Electronic books
• Languages: English
• ISBNs: 9789813291669; 9813291664
• Related ISBNs: 9789813291652
• Notes: Note: Includes bibliographical references.; Note: Online resource; title from PDF title page (SpringerLink, viewed September 11, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.