Genetic Algorithm-Based Pattern Classification: Relationship with Bayes Classifier

2017 ◽  
pp. 127-144
Author(s):  
Chivukula A. Murthy ◽  
Sanghamitra Bandyopadhyay ◽  
Sankar K. Pal
Keyword(s):  
Genetic Algorithm ◽  
Pattern Classification ◽  
Bayes Classifier

PROJECT CellNet: EVOLVING AN AUTONOMOUS PATTERN RECOGNIZER

2004 ◽  
Vol 18 (06) ◽  
pp. 1039-1056 ◽  
Author(s):  
N. KHARMA ◽  
T. KOWALIW ◽  
E. CLEMENT ◽  
C. JENSEN ◽  
A. YOUSSEF ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Pattern Classification ◽  
Black Box ◽  
Global Changes ◽  
Software System ◽  
Human Intervention ◽  
Evolutionary System ◽  
Training Set ◽  
Binary Classifiers ◽  
Standard Genetic Algorithm

We describe the desire for a black box approach to pattern classification: a generic Autonomous Pattern Recognizer, which is capable of self-adapting to specific alphabets without human intervention. The CellNet software system is introduced, an evolutionary system that optimizes a set of pattern-recognizing agents relative to a provided set of features and a given pattern database. CellNet utilizes a new genetic operator designed to facilitate a canalization of development: Merger. CellNet utilizes our own set of arbitrarily chosen features, and is applied to the CEDAR Database of handwritten Latin characters, as well as to a database of handwritten Indian digits provided by CENPARMI. CellNet's cooperative co-evolutionary approach shows significant improvement over a more standard Genetic Algorithm, both in terms of efficiency and in nearly eliminating over-fitting (to the training set). Additionally, the binary classifiers autonomously evolved by CellNet return validation accuracies approaching 98% for both Latin and Indian digits, with no global changes to the system between the two trials.

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