#rough sets

[ijtsrd]

An Enhanced Feature Selection Method to Predict the Severity in Brain Tumor

by Parthiban J | Dr. B. Sathees Kumar ""An Enhanced Feature Selection Method to Predict the Severity in Brain Tumor""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,

URL: https://www.ijtsrd.com/papers/ijtsrd26802.pdf  

Paper URL: https://www.ijtsrd.com/computer-science/data-miining/26802/an-enhanced-feature-selection-method-to-predict-the-severity-in-brain-tumor/parthiban-j

pharmacy journal, open access journal of engineering, research publication

The level of severity of brain tumor is captured through MRI and then assessed by the physician for their medical interpretation. The facts behind the MRI images are then analyzed by the physician for further medication and follow up activities. An MRI image composed of large volume of features. It has irrelevant, missing and information which is not certain. In medical data analysis, an MRI image doesn't express facts very clearly to the physician for correct interpretation all the time. It also includes huge amount of redundant information within it. A mathematical model known as rough set theory has been applied to resolve this problem by eliminating the redundancy in medical image data. This paper uses a rough set method to find the severity level of the brain tumor of the given MRI image. Rough set feature selection algorithms are applied over the medical image data to select the prominent features. The classification accuracy of the brain tumor can be improved to a better level by using this rough set approach. The prominent features selected through this approach deliver a set of decision rules for the classification task. A search method based on the particle swarm optimization is proposed in this paper for minimizing the attribute set. This approach is compared with previously existing rough set reduction algorithm for finding the accuracy. The reducts originated from the proposed algorithm is more efficient and can generate decision rules that will better classify the tumor types. The rule based method provided by the rough set method delivers classification accuracy in higher level than other smart methods such as fuzzy rule extraction, neural networks, decision trees and Fuzzy Networks like Fuzzy Min Max Neural Networks. 

[vidadqgn-blog]

Rough Sets and Knowledge Technology: 4th International Conference, RSKT 2009, Gold Coast, Australia, July 14-16, 2009, Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence)

This book constitutes the refereed proceedings of the Fourth International Conference on Rough Sets and Knowledge Technology, RSKT 2009, held in Gold Coast, Australia, in July 2009. The 85 revised full papers presented together with 3 keynote papers and 2 special sessions were carefully reviewed and selected from 229 submissions. The papers are organized in topical sections on rough sets and computing, rough sets and data reduction, data mining and knowledge discovery, Rough Sets and Knowledge Technology: 4th International Conference, RSKT 2009, Gold Coast, Australia, July 14-16, 2009, Proceedings (Lecture Notes in ... / Lecture Notes in Artificial Intelligence) granular computing and cognitive computing, fuzzy sets and computing, knowledge technology and intelligent systems, computational intelligence and applications, image processing and understanding, and formal concept analysis.

[musebaz-blog]

Data Mining, Rough Sets and Granular Computing

Data Mining, Rough Sets and Granular Computing This volume is the result of a two-year project aimed at coalescing the concepts and techniques of granular computing on one side, and rough set theory on another. It consists of a collection of up-to-date and authoritative expositions of the basic theories underlying data mining, granular computing and rough set theory, and stresses their wide-ranging applications. A principal aim of the work is to stimulate an exploration of ways in which progress in data mining can be enhanced through integration with granular computing and rough set theory.

[lyalljbcw027-blog]

Transactions on Rough Sets II: Rough Sets and Fuzzy Sets (Lecture Notes in Computer Science / Transactions on Rough Sets) (v. 2)

Transactions on Rough Sets II: Rough Sets and Fuzzy Sets (Lecture Notes in Computer Science / Transactions on Rough Sets) (v. 2) The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, starting from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness and incompleteness, such as fuzzy sets and theory of evidence. This second volume of the Transactions on Rough Sets presents 17 thoroughly reviewed revised papers devoted to rough set theory, fuzzy set theory; these papers highlight important aspects of these theories, their interrelation and application in various fields. Transactions on Rough Sets II: Rough Sets and Fuzzy Sets (Lecture Notes in Computer Science / Transactions on Rough Sets) (v. 2)