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Application of genetic algorithms and multiple hypotheses for supervised machine learning.
| Content Provider | CiteSeerX |
|---|---|
| Author | Gallege, Sajika |
| Abstract | This paper describes the implementation of a Genetic Algorithm (GA) and a variant of learning multiple hypotheses. The GA is applied to a binary classification of a labeled dataset to evaluate the algorithm’s performance in a supervised learning environment. The details include the basic GA implementation and the modifications done to create a variant of learning multiple hypotheses. The Paper also includes the results and a comparison to other Machine Learning Algorithms such as Decision Trees and Perceptrons. The discussion includes advantages such as the ability to parallelize the hypotheses search and drawbacks such as the time taken to select good hypotheses as well as propose modifications to enhance the algorithm. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Multiple Hypothesis Genetic Algorithm Supervised Machine Learning Binary Classification Basic Ga Implementation Machine Learning Algorithm Hypothesis Search Propose Modification Decision Tree Labeled Dataset Good Hypothesis Supervised Learning Environment Algorithm Performance |
| Content Type | Text |
| Resource Type | Article |
