Conference proceeding
Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), Vol.31
Advances in Neural Information Processing Systems
01/01/2018
Abstract
We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-value rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-value rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating an MRS model and develop an efficient inference method for learning a maximum a posteriori, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments on synthetic and real-world data demonstrate that MRS models have significantly smaller complexity and fewer features than baseline models while being competitive in predictive accuracy. Human evaluations show that MRS is easier to understand and use compared to other rule-based models.
Details
- Title: Subtitle
- Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
- Creators
- Tong Wang - Univ Iowa, Tippie Sch Business, Iowa City, IA 52242 USA
- Contributors
- S Bengio (Editor)H Wallach (Editor)H Larochelle (Editor)K Grauman (Editor)N CesaBianchi (Editor)R Garnett (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), Vol.31
- Publisher
- Neural Information Processing Systems (Nips)
- Series
- Advances in Neural Information Processing Systems
- ISSN
- 1049-5258
- Number of pages
- 11
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380410802771
Metrics
2 Record Views