Gene Selection in Microarray Datasets Using Progressively Refined PSO Scheme
Authors: Yamuna Prasad, K. Biswas
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on publicly available datasets, Colon, Leukemia and T2D show that our approach selects only a very small subset of genes while yielding substantial improvements in accuracy over state-of-the-art evolutionary methods. |
| Researcher Affiliation | Academia | Indian Institute of Technology Delhi New Delhi, India 110016 {yprasad, kkb}@cse.iitd.ac.in |
| Pseudocode | Yes | Algorithm 1: Progressively Refined PSO |
| 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 experiment with five publicly available benchmark microarray datasets, namely Colon, Lymphoma, Leukemia, RAOA and T2D (Ganesh K. et al. 2012). |
| Dataset Splits | Yes | We followed training and test splitting of (Li et al. 2008) and (Ganesh K. et al. 2012) for 10CV and LOOCV strategies respectively. ... The cost parameter C in SVM is tuned using 5-fold cross-validation on training dataset only for computing fitness of a particle in PSO. |
| Hardware Specification | No | The paper does not provide specific hardware details like CPU/GPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions using Linear SVM (Fan et al. 2008) but does not provide specific version numbers for any software components or libraries. |
| Experiment Setup | Yes | For PSO implementation, we used C1 = 2, C2 = 2, ω =0.9, K=40 and T=100 (Li et al. 2008). The maximum number of iterative depth D is set to 20 in PRPSO method. |