scholarly journals A genetic algorithm based nearest neighbor classification to breast cancer diagnosis

2003 ◽  
Vol 26 (1) ◽  
pp. 6-11 ◽  
Author(s):  
R. Jain ◽  
J. Mazumdar
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Cancer Diagnosis ◽  
Nearest Neighbor ◽  
Breast Cancer Diagnosis ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification

2000 ◽  
Vol 4 (2) ◽  
pp. 138-145 ◽  
Author(s):  
Hisao Ishibuchi ◽  
◽  
Tomoharu Nakashima
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Computer Simulations ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Data Sets ◽  
Nearest Neighbor Classification ◽  
Reference Set ◽  
Neighbor Classification

This paper proposes a genetic-algorithm-based approach for finding a compact reference set in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A binary string is used for representing the inclusion (or exclusion) of each pattern and feature in the reference set. Our goal is to minimize the number of selected patterns, to minimize the number of selected features, and to maximize the classification performance of the reference set. Computer simulations on commonly used data sets examine the effectiveness of our approach.

Self-Adapting Intelligent Neural Systems Using Evolutionary Techniques

2006 ◽  
pp. 94-115
Author(s):  
Daniel Manrique ◽  
Juan Rios ◽  
Alfonso Rodriguez-Paton
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Cancer Diagnosis ◽  
Laboratory Tests ◽  
Breast Cancer Diagnosis ◽  
Search Space ◽  
Neural Systems ◽  
Multilayer Perceptrons ◽  
Evolutionary System ◽  
Real World Problem

This chapter describes genetic algorithm-based evolutionary techniques for automatically constructing intelligent neural systems. These techniques can be used to build and train multilayer perceptrons with the simplest architecture. These neural networks are usually designed using binary-coded genetic algorithms. The authors show how the basic architectures codification method, which uses an algebra-based codification, employs a shorter string length and voids illegal architectures in the search space. The networks are trained using real number codification. The morphological crossover operator is presented and compared to other important real-coded crossover operators. The purpose is to understand that the combination of all these techniques results in an evolutionary system, which self-adaptively constructs intelligent neural systems to solve a problem given as a set of training patterns. To do so, the evolutionary system is applied in laboratory tests and to a real-world problem: breast cancer diagnosis.

Sign in / Sign up

Export Citation Format

Share Document