A carregar...
Publicação
Layered genetic programming for feature extraction in classification problems
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.69 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Genetic programming has been proven to be a successful technique for feature extraction in various
applications. In this thesis, we present a Layered Genetic Programming system which implements
genetic programming-based feature extraction mechanism. The proposed system uses a layered
structure where instead of evolving just one population of individuals, several populations are evolved
sequentially. Each such population transforms the input data received from the previous population
into a lower dimensional space with the aim of improving classification performance.
The performance of the proposed system was experimentally tested on 5 real-world problems using
different dimensionality reduction step sizes and different classifiers. The proposed method was able
to outperform a simple classifier applied directly on the original data on two problems. On the
remaining problems, the classifier performed better using the original data. The best solutions were
often obtained in the first few layers which implied that increasing the size of the system, i.e. adding
more layers was not useful. However, the layered structure allowed control of the size of individuals.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Genetic Programming Feature Extraction Dimensionality Reduction Classification