Hybrid Fuzzy Rule Based Classification Algorithm
HYBRID FUZZY RULE BASED CLASSIFICATION ALGORITHM Introduction
1.1 Purpose
The purpose of this document is to design a strategy for hybrid fuzzy rule base classification algorithm using the weka tool. This document outlines the functional requirements for hybrid fuzzy rule based classification algorithm. This document discusses the project’s goals and parameters, while giving descriptions about the potential design issues. The requirements are specified according to the finished product.
1.2 Document Conventions
This document has been written on the following style Font style Headings Sub – Headings Data Line Spacing Times New Roman 16 Bold 14 Bold 12 Regular 1.5 Lines
1
1.3 Intended Audience and Reading Suggestions
The document will have a wide application in which data mining solutions is required. Besides, researchers who are interested in the field of Data mining will find this system as a useful tool.
1.4 Project scope
The purpose of the project is to hybridize the underlying concepts of fuzzy rule base classification algorithm to deal with additional aspects of data imperfection. Objective of the project is to integrate different models of fuzzy rules-based classification algorithm so as to bring out a new algorithm. Goal of the project is to produce better accuracy than the other models being used for classification.
2
CHAPTER II
2. Overall Description
2.1 Product Perspective
Classification is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance. There are various models available for doing classification like FuzzyNN, FuzzyRoughNN,NN, and FuzzyOwnershipNN etc. In this research work an attempt is made to integrate these models to bring out a more efficient model that can inherit the properties of these models and provide better performance in classification process.
2.2 Product Features
The system has four modules. o o o o Creating an Instance to load input data
1.1 Purpose
The purpose of this document is to design a strategy for hybrid fuzzy rule base classification algorithm using the weka tool. This document outlines the functional requirements for hybrid fuzzy rule based classification algorithm. This document discusses the project’s goals and parameters, while giving descriptions about the potential design issues. The requirements are specified according to the finished product.
1.2 Document Conventions
This document has been written on the following style Font style Headings Sub – Headings Data Line Spacing Times New Roman 16 Bold 14 Bold 12 Regular 1.5 Lines
1
1.3 Intended Audience and Reading Suggestions
The document will have a wide application in which data mining solutions is required. Besides, researchers who are interested in the field of Data mining will find this system as a useful tool.
1.4 Project scope
The purpose of the project is to hybridize the underlying concepts of fuzzy rule base classification algorithm to deal with additional aspects of data imperfection. Objective of the project is to integrate different models of fuzzy rules-based classification algorithm so as to bring out a new algorithm. Goal of the project is to produce better accuracy than the other models being used for classification.
2
CHAPTER II
2. Overall Description
2.1 Product Perspective
Classification is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance. There are various models available for doing classification like FuzzyNN, FuzzyRoughNN,NN, and FuzzyOwnershipNN etc. In this research work an attempt is made to integrate these models to bring out a more efficient model that can inherit the properties of these models and provide better performance in classification process.
2.2 Product Features
The system has four modules. o o o o Creating an Instance to load input data
References: • • • • • • • • • • • 1. D. Aha, Instance-based learning algorithm, Machine Learning, vol. 6, pp. 37{66, 1991. 2. R.B. Bhatt and M.Gopal, FRID: Fuzzy-Rough Interactive Dichotomizers, IEEE International Conference on Fuzzy Systems (FUZZ-IEEE '04), pp. 1337{1342, 2004. 3. H. Bian and L. Mazlack, Fuzzy-Rough Nearest-Neighbor Classication Approach, Proceeding of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 500{505, 2003. 4. C.L. Blake and C.J. Merz. UCI Repository of machine learning databases. Irvine, University of California, 1998. http://archive.ics.uci.edu/ml/ 5. W.W. Cohen, Fast e ective rule induction, In Machine Learning: Proceedings of the 12th International Conference, pp. 115. 14