On the Role of Genetic Algorithms in the Pattern Recognition Task of Classification (original) (raw)

Using genetic algorithms to improve pattern classification performance

Proceedings of the 1990 conference on …, 1990

Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech pattern classification tasks. For a complex speech recognition task, genetic algorithms required no more computation time than traditional approaches to feature selection but reduced the number of input features required by a factor of five (from 153 to 33 features). On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) which reduced classification error rates from 19% to almost 0%. Neural net and k nearest neighbor (KNN) classifiers were unable to provide such low error rates using only the original features. Genetic algorithms were also used to reduce the number of reference exemplar patterns for a KNN classifier. On a 338 training pattern vowel-recognition problem with 10 classes, genetic algorithms reduced the number of stored exemplars from 338 to 43 without significantly increasing classification error rate. In all applications, genetic algorithms were easy to apply and found good solutions in many fewer trials than would be required by exhaustive search. Run times were long, but not unreasonable. These results suggest that genetic algorithms are becoming practical for pattern classification problems as faster serial and parallel computers are developed.

Classifier Systems & Genetic Algorithms

extracted from chapter 3 of "ZEROTH-ORDER SHAPE OPTIMIZATION UTILIZING A LEARNING CLASSIFIER SYSTEM" from Robert A. Richards -http://www.stanford.edu/\~buc/SPHINcsX/book.html) Soon after the advent of the electronic computer, scientists envisioned its potential to exhibit learning behavior. Since the early machine learning work by , many machine learning systems have been developed. One of these, the learning classifier system, introduced by Holland and Reitman [1978] is a machine learning system which possesses the salient properties needed to learn in the shape optimization domain.

A survey on the application of genetic programming to classification

2010

Abstract Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of evolutionary algorithms for training classifiers has been studied in the past few decades.

Genetic Algorithms for Classification and Feature Extraction

Annual Meeting: Classification …, 1995

Min Pei, 1,2 Erik D. Goodman, 2 William F. Punch III 3 and Ying Ding 2 ... 1 Beijing Union University, Beijing, China 2 Case Center for Computer-Aided Engineering and Manufacturing 3 Intelligent Systems Laboratory, Department of Computer Science

A Genetic Programming Approach to Binary Classification Problem

EAI Endorsed Transactions on Energy Web, 2018

The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.

Classification of signals by means of Genetic Programming

Soft Computing, 2013

This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.

Genetic Programming Symbolic Classification: A Study

2017

While Symbolic Regression (SR) is a well-known offshoot of Genetic Programming, Symbolic Classification (SC), by comparison, has received only meager attention. Clearly, regression is only half of the solution. Classification also plays an important role in any well rounded predictive analysis tool kit. In several recent papers, SR algorithms are developed which move SR into the ranks of extreme accuracy. In an additional set of papers algorithms are developed designed to push SC to the level of basic classification accuracy competitive with existing commercially available classification tools. This paper is a simple study of four proposed SC algorithms and five well-known commercially available classification algorithms to determine just where SC now ranks in competitive comparison. The four SC algorithms are: simple genetic programming using argmax referred to herein as (AMAXSC); the M2GP algorithm; the MDC algorithm, and Linear Discriminant Analysis (LDA). The five commercially a...

Genetic Algorithm and Programming Based Classification: A Survey

Classification is the process of finding a model or a function that describes and distinguishes data classes and concepts, for the purpose of being able to use the model to predict the classes of objects whose class label is not known. The process of data analysis becomes time consuming and tedious as volume of data increases. So to make the process of data classification faster, soft computing techniques have been applied. Great deal of work has been done in the area of classification using evolutionary techniques. This survey gives an insight into the work done on classification using genetic algorithms and genetic programming and their applications in different problems and areas.