Name
Abstract | ||
|---|---|---|
In dealing with a very large data set, it might be impractical to construct a decision tree using all of the points. Even when it is possible, this might not be the best way to utilize the data. As an alternative, subsets of the original data set can be extracted, a tree can be constructed on each subset, and then parts of individual trees can be combined in a smart way to produce an improved final set of feasible trees or a final tree. In this paper, we take trees generated by a commercial decision tree package, namely, C4.5, and allow them to crossover and mutate (using a genetic algorithm) for a number of generations in order to yield trees of better quality. We conduct a computational study of our approach using a real-life marketing data set. In this study, we divide the data set into training, scoring, and test sets, and find that our approach produces uniformly high-quality decision trees. In addition, we investigate the impact of scaling and demonstrate that our approach can be used effectively on very large data sets. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1287/ijoc.15.1.3.15152 | INFORMS Journal on Computing |
Keywords | Field | DocType |
original data,final tree,large data,large data set,improved final set,building accurate decision trees,decision tree,commercial decision tree package,test set,real-life marketing data,genetic algorithm-based approach,feasible tree,genetic algorithm | Data mining,Tree rearrangement,Metric tree,Weight-balanced tree,Artificial intelligence,ID3 algorithm,Mathematics,Decision tree learning,Machine learning,Alternating decision tree,Incremental decision tree,Search tree | Journal |
Volume | Issue | ISSN |
15 | 1 | 1091-9856 |
Citations | PageRank | References |
14 | 0.78 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwei Fu | 1 | 38 | 3.80 |
| Bruce L. Golden | 2 | 1110 | 176.17 |
| Shreevardhan Lele | 3 | 54 | 5.09 |
| S. Raghavan | 4 | 216 | 16.30 |
| Edward A. Wasil | 5 | 898 | 85.07 |