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Fast feature ranking algorithm
| Content Provider | CiteSeerX |
|---|---|
| Author | Ruiz, Roberto Riquelme, José C. Aguilar-Ruiz, Jesús S. |
| Abstract | Abstract. The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. In order to test the relevance of the new feature selection algorithm, we compare the results induced by several classifiers before and after applying the feature selection algorithms. 1 |
| File Format | |
| Journal | Knowledge-Based Intelligent Information and Engineering Systems |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Fast Feature Attribute Selection Technique Discrete Variable Typical Algorithm Classification Simpler Several Classifier New Feature Selection Algorithm Interesting Characteristic Data Set Feature Selection Algorithm Relevant Attribute Preprocessing Phase Statistical Calculation Labelled Data Set Computational Cost Supervised Learning |
| Content Type | Text |
| Resource Type | Article |
