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A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method
"... Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system a ..."
Abstract
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Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.
Feature Manipulation with Genetic Programming
, 2010
"... Feature manipulation refers to the process by which the input space of a machine learning task is altered in order to improve the learning quality and performance. Three major aspects of feature manipulation are feature construction, feature ranking and feature selection. This thesis proposes a new ..."
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Feature manipulation refers to the process by which the input space of a machine learning task is altered in order to improve the learning quality and performance. Three major aspects of feature manipulation are feature construction, feature ranking and feature selection. This thesis proposes a new filter-based methodology for feature manipulation in classification problems using genetic programming (GP). The goal is to modify the input representation of classification problems in order to improve classification performance and reduce the complexity of classification models. The thesis regards classification problems as a collection of variables including conditional variables (input features) and decision variables (target class labels). GP is used to discover the relationships between these variables. The types of relationship and the ways in which they are discovered vary with the three aspects of feature manipulation. In feature construction, the thesis proposes a GP-based method to construct high-level features in the form of functions of original input features.
