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..
Multi-Label Informed Feature Selection
Authors: Ling Jian, Jundong Li, Kai Shu, Huan Liu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies on real-world datasets demonstrate the effectiveness and efficiency of the proposed framework. |
| Researcher Affiliation | Academia | 1. Computer Science and Engineering, Arizona State University, Tempe, 85281, USA 2. College of Science, China University of Petroleum, Qingdao, 266555, China |
| Pseudocode | Yes | The pseudocode of the multi-label informed feature selection framework MIFS is illustrated in Algorithm 1. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of its own source code for the methodology. |
| Open Datasets | Yes | Experiments are conducted on four publicly available benchmark datasets1, including one image dataset (i.e. Scene [Boutell et al., 2004]) and three text datasets from RCV1 [Lewis et al., 2004]. 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | Yes | To have a fair comparison with existing methods, we decompose the multi-labeled classification problem into multiple binary classification problems, and then employ SVM to learn these binary classifiers with a five-fold cross validation. Table 1: Details of four benchmark datasets. Dataset # Training # Test # Features # Labels |
| Hardware Specification | No | The paper discusses running time and computational efficiency but does not specify any hardware details (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions using 'Liblinear toolbox' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | In MIFS, there are some parameters need to be set in advance. First, to model the local geometry structure in the input space X, we set the parameters σ2 and p as 1 and 5, respectively. There are three important regularization parameters λ, β and γ in MIFS. For all these methods, we report the best results of the optimal parameters in terms of classification performance. The experiments are repeated 5 times and averaged. |