CORRESPONDENCE: Pattern RecognitionNo Access

PROJECT CellNet: EVOLVING AN AUTONOMOUS PATTERN RECOGNIZER

Departments of Electrical and Computer Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd W, Montréal, Canada H3G 1M8, Canada

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T. KOWALIW

Departments of Electrical and Computer Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd W, Montréal, Canada H3G 1M8, Canada

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E. CLEMENT

Departments of Electrical and Computer Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd W, Montréal, Canada H3G 1M8, Canada

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C. JENSEN

Departments of Electrical and Computer Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd W, Montréal, Canada H3G 1M8, Canada

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A. YOUSSEF

Departments of Electrical and Computer Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd W, Montréal, Canada H3G 1M8, Canada

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, and

J. YAO

Departments of Electrical and Computer Engineering and Computer Science, Concordia University, 1455 de Maisonneuve Blvd W, Montréal, Canada H3G 1M8, Canada

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https://doi.org/10.1142/S0218001404003587Cited by:6 (Source: Crossref)

Abstract

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.

Keywords:

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