Multiple Indefinite Kernel Learning for Feature Selection
Authors: Hui Xue, Yu Song, Hai-Ming Xu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets demonstrate that MIK-FS is superior to some related state-of-the-art methods in both feature selection and classification performance. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China 2MOE Key Laboratory of Computer Network and Information Integration (Southeast University), China |
| Pseudocode | Yes | Algorithm 1 MIK-FS; Algorithm 2 Primal IKSVM; Algorithm 3 PGD |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We select ten datasets from three different repositories for experiments: (a) seven datasets from a feature selection repository1, namely ALLAML, Colon, Gli 85, Prostate GE, Isolet, Glioma, Carcinom; (b) two binary datasets from an online repository2 of high-dimensional biomedical datasets, namely Central Nervous System, Lung Cancer; (c) one dataset Dbworld from UCI Machine Learning Repository. (Footnote 1: http://featureselection.asu.edu/datasets.php; Footnote 2: http://datam.i2r.a-star.edu.sg/datasets/krbd/) |
| Dataset Splits | Yes | We randomly divide the samples into two non-overlapping training and testing sets which contain almost half of the samples in each class. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions). |
| Experiment Setup | Yes | For all the algorithms, the regularization parameters and the kernel parameters are chosen from the set {10 2, 10 1, 1, 101, 102}. A feature is discarded if the corresponding kernel combination coefficient di is less than a small threshold, e.g., 10 5. We choose the indefinite sigmoid kernel k = tanh(a x T i xj r) as the base feature kernel in MIK-FS. Gaussian kernel is used for two MKL-FS methods. |