Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View
Authors: Nguyen Vinh, Jeffrey Chan, James Bailey
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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. |