Name
Abstract | ||
|---|---|---|
Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1162/neco.2006.18.9.2062 | Neural Computation |
Keywords | Field | DocType |
function approximation task,local decision hyperplanes,data sample,pattern classification,online adaptive decision trees,function approximation,two-class classification task,multiclass classification task,classification model,generalization score,decision tree,structural similarity,multiclass classification,rule based,gradient descent | Decision tree,Terminal and nonterminal symbols,Gradient descent,Function approximation,Models of neural computation,Artificial intelligence,Tree structure,Artificial neural network,Mathematics,Machine learning,Multiclass classification | Journal |
Volume | Issue | ISSN |
18 | 9 | 0899-7667 |
Citations | PageRank | References |
12 | 1.19 | 33 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayanta Basak | 1 | 372 | 32.68 |