Multi-Label Informed Feature Selection
Authors: Ling Jian, Jundong Li, Kai Shu, Huan Liu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |