Name
Abstract | ||
|---|---|---|
In this paper, genetic algorithms are used in machine learning classification task. They act as a constructive induction engine,
which selects features and adjusts weights of attributes, in order to obtain the highest classification accuracy. We compare
two classification approaches: a single 1-NN and a n
2 meta-classifier. For the n
2-classifier, the idea of an improvement of classification accuracy is based on independent modification of descriptions of
examples for each pair of n classes. Finally, it gives (n
2−n)/2 spaces of attributes dedicated for discrimination of pairs of classes. The computational experiment was performed on a
medical data set. Its results reveal the utility of using genetic algorithms for features selection and weight adjusting,
and point out the advantage of using a multi-classification model (n
2-classifier) with constructive induction in relation to the analogous single-classifier approach.
|
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/BFb0095156 | ISMIS |
Keywords | Field | DocType |
feature selection,genetic algorithms,multi/meta-classification systems.,knowledge representa- tion,evolutionary computation,machine learning,constructive induction,genetic algorithm,evolutionary computing,classification system,computer experiment | Constructive induction,Knowledge representation and reasoning,Feature selection,Computer science,Evolutionary computation,Algorithm,Learning by example,Artificial intelligence,Statistical classification,Machine learning,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
3-540-65965-X | 1 | 0.39 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacek Jelonek | 1 | 126 | 16.49 |
| Maciej Komosinski | 2 | 116 | 17.33 |