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..
Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View
Authors: Nguyen Vinh, Jeffrey Chan, James Bailey
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on 2 multi-view datasets and 3 single-view datasets. The summary of these datasets are given in Table 1. ... The experimental results are represented in Table 2. |
| Researcher Affiliation | Academia | Cam-Tu Nguyen1,3, Xiaoliang Wang1, Jing Liu2 and Zhi-Hua Zhou1 1 National Key Laboratory for Novel Software Technology, Nanjing University, 210023, China 2 National Key Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, China 3 VNU-University of Engineering and Technology, Hanoi, Vietnam |
| Pseudocode | Yes | Algorithm 1 Generative Process for MIMLmix, Algorithm 2 Training with MIMLmix, Algorithm 3 Testing with MIMLmix |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Citeseerx-10k has 1072 partial examples, and Image CLEF has 2114 partial examples; most of partial examples collected from http://citeseerx.ist.psu.edu/index |
| Dataset Splits | Yes | We conduct 30 times evaluation for Image CLEF, each time we use 1000 examples for training and 1000 examples for testing; 10-fold crossvalidation is conducted for the other datasets. |
| Hardware Specification | Yes | Table 4 shows training times of MIML methods on 3 large datasets on the same computer (CPU of 3.3Gz, 4GB memory). |
| Software Dependencies | No | The paper mentions 'LIBSVM (Chang and Lin 2011)' but does not specify version numbers for LIBSVM or any other software dependencies. |
| Experiment Setup | Yes | For MIMLmix methods, we set 0 = 0.1, K = 200 as default for both datasets, set = .3, = 5 for Citeseerx-10k; and = 10 and = 0 for Image CLEF. ... We train Rank Loss SVM with default values, train MIMLSVM and cs-MISVM with RBF kernel with C=23 and γ=.5. |