Name
Abstract | ||
|---|---|---|
Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher's Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher's linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way. The Fisher's decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2013.05.044 | Expert Syst. Appl. |
Keywords | Field | DocType |
oblique tree,decision boundary,public available data,artificial attribute,decision tree,oblique decision tree,decomposition strategy,dimensionality reduction,data mining application,univariate decision tree | Data mining,Decision tree,Computer science,Artificial intelligence,ID3 algorithm,Alternating decision tree,Decision stump,Pattern recognition,Linear discriminant analysis,Univariate,Machine learning,Decision tree learning,Incremental decision tree | Journal |
Volume | Issue | ISSN |
40 | 16 | 0957-4174 |
Citations | PageRank | References |
10 | 0.71 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asdrúbal López Chau | 1 | 87 | 11.62 |
| Jair Cervantes | 2 | 176 | 18.08 |
| Lourdes LóPez-GarcíA | 3 | 10 | 0.71 |
| Farid GarcíA Lamont | 4 | 69 | 9.58 |