Variational Information Maximization for Feature Selection
Authors: Shuyang Gao, Greg Ver Steeg, Aram Galstyan
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that the proposed method strongly outperforms existing information-theoretic feature selection approaches. Our experiments demonstrate that the proposed method strongly outperforms existing information-theoretic feature selection approaches. We also conduct empirical validation on various datasets and demonstrate that the proposed approach outperforms state-of-the-art information-theoretic feature selection methods. |
| Researcher Affiliation | Academia | Shuyang Gao Greg Ver Steeg Aram Galstyan University of Southern California, Information Sciences Institute gaos@usc.edu, gregv@isi.edu, galstyan@isi.edu |
| Pseudocode | No | The paper mentions that |
| Open Source Code | Yes | Shuyang Gao. Variational feature selection code. http://github.com/Biu Biu Bi LL/ Info Feature Selection. |
| Open Datasets | Yes | We use 17 well-known datasets in previous feature selection studies [5, 12] (all data are discretized). The dataset summaries are illustrated in supplementary Sec. C. We use the average cross-validation error rate on the range of 10 to 100 features to compare different algorithms under the same setting as [12]. Tenfold cross-validation is employed for datasets with number of samples N 100 and leave-one-out cross-validation otherwise. The 3-nearest-neighbor classifier is used for Gisette and Madelon, following [5]. For the remaining datasets, the chosen classifier is Linear SVM, following [11, 12]. [26] Kevin Bache and Moshe Lichman. Uci machine learning repository, 2013. |
| Dataset Splits | Yes | Tenfold cross-validation is employed for datasets with number of samples N 100 and leave-one-out cross-validation otherwise. The 3-nearest-neighbor classifier is used for Gisette and Madelon, following [5]. For the remaining datasets, the chosen classifier is Linear SVM, following [11, 12]. |
| Hardware Specification | No | No specific hardware details (GPU, CPU models, memory, etc.) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were provided. |
| Experiment Setup | Yes | We use the average cross-validation error rate on the range of 10 to 100 features to compare different algorithms under the same setting as [12]. Tenfold cross-validation is employed for datasets with number of samples N 100 and leave-one-out cross-validation otherwise. The 3-nearest-neighbor classifier is used for Gisette and Madelon, following [5]. For the remaining datasets, the chosen classifier is Linear SVM, following [11, 12]. |