|
| << vorige | volgende >> |
Tijdschrift beschrijving
Alle jaargangen van het bijbehorende tijdschrift
Alle afleveringen van het bijbehorende jaargang
Alle artikelen van de bijbehorende aflevering
Details van artikel 3 van 10 gevonden artikelen
| Titel: |
Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins |
| Auteur: |
Pappa, Gisele L. Freitas, Alex A. |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 13 (2009) nr. 2 pagina's 243-259 |
| Jaar: |
2009-04-22 |
| Inhoud: |
It is well-known that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this approach, consisting of automatically creating a rule induction algorithm tailored to the target application domain. This work proposes a grammar-based genetic programming (GGP) system to perform "algorithm construction". The GGP is used to build a complete rule induction algorithm tailored to 5 well-known UCI data sets and a protein data set, where the goal is to predict whether or not a protein presents postsynaptic activity. The results show that the rule induction algorithms automatically constructed by the GGP are competitive with well-known human-designed rule induction algorithms. Moreover, in the postsynaptic case study, the GGP was more successful than the human-designed algorithms in discovering accurate rules predicting the minority class – whose prediction is more difficult and tends to be more important to the user than the prediction of the majority class. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 10 gevonden artikelen
| << vorige | volgende >> |