Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Genetic algorithms re 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 re 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 re unable to provide such low error rates using only the original features. Genetic algorithms re also used to reduce the number of reference exemplar patterns for a KNN classifier. On a 338 training pattern vol-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 re easy to apply and found good solutions in many fer trials than would be required by exhaustive search. Run times re 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.
['Eric I. Chang', 'Richard P. Lippmann']
I am writing a research paper on a idea Using Genetic Algorithms to Improve Pattern Classification Performance
[{"from": "human", "value": "I am writing a research paper on a idea Using Genetic Algorithms to Improve Pattern Classification Performance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Genetic algorithms re 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 re 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 re unable to provide such low error rates using only the original features. Genetic algorithms re also used to reduce the number of reference exemplar patterns for a KNN classifier. On a 338 training pattern vol-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 re easy to apply and found good solutions in many fer trials than would be required by exhaustive search. Run times re 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."}]