Name
Abstract | ||
|---|---|---|
The Probabilistic random forest is a classification model which chooses a subset of features for each random forest depending on the F-score of the features. In other words, the probability of a feature being chosen in the feature subset increases as the F-score of the feature in the dataset. A larger F-score of feature indicates that feature is more discriminative. The features are drawn in a stochastic manner and the expectation is that features with higher F-score will be in the feature subset chosen. The class label of patterns is obtained by combining the decisions of all the decision trees by majority voting. Experimental results reported on a number of benchmark datasets demonstrate that the proposed probabilistic random forest is able to achieve better performance, compared to the random forest. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SSCI.2015.35 | 2015 IEEE Symposium Series on Computational Intelligence |
Keywords | Field | DocType |
classification model,F-score,feature subset,pattern class label,decision trees,majority voting,probabilistic random forest | Decision tree,Pattern recognition,Computer science,Artificial intelligence,Probabilistic logic,Random forest,Majority rule,Discriminative model,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4799-7560-0 | 0 | 0.34 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| rajhans gondane | 1 | 0 | 0.34 |
| V. Susheela Devi | 2 | 47 | 9.21 |