Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Gene Selection in Microarray Datasets Using Progressively Refined PSO Scheme
Authors: Yamuna Prasad, K. Biswas
AAAI 2015 | Venue PDF | 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 EMAIL |
| 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. |