- (Loscalzo et al., 2009) ⇒ Steven Loscalzo, Lei Yu, and Chris Ding. (2009). “Consensus Group Stable Feature Selection.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557084
- Categories and Subject Descriptors: H.2.8 Database Management: Database Applications - data mining; I.2.6 Artificial Intelligence: Learning.
- General Terms: Algorithms
Stability is an important yet under-addressed issue in feature selection from high-dimensional and small sample data. In this paper, we show that stability of feature selection has a strong dependency on sample size. We propose a novel framework for stable feature selection which first identifies consensus feature groups from subsampling of training samples, and then performs feature selection by treating each consensus feature group as a single entity. Experiments on both synthetic and real-world data sets show that an algorithm developed under this framework is effective at alleviating the problem of small sample size and leads to more stable feature selection results and comparable or better generalization performance than state-of-the-art feature selection algorithms. Synthetic data sets and algorithm source code are available at http://www.cs.binghamton.edu/~lyu/KDD09/.
|2009 ConsensusGroupStableFeatureSele||Steven Loscalzo|
|Consensus Group Stable Feature Selection||KDD-2009 Proceedings||10.1145/1557019.1557084||2009|