Name
Abstract | ||
|---|---|---|
Model interpretability has been recognized to play a key role in practical data mining. Interpretable models provide significant insights on data and model behaviors and may convince end-users to employ certain models. In return for these advantages, however, there is generally a sacrifice in accuracy, i.e., flexibility of model representation (e.g., linear, rule-based, etc.) and model complexity needs to be restricted in order for users to be able to understand the results. This paper proposes oblique treed sparse additive models (OT-SpAMs). Our main focus is on developing a model which sacrifices a certain degree of interpretability for accuracy but achieves entirely sufficient accuracy with such fully non-linear models as kernel support vector machines (SVMs). OT-SpAMs are instances of region-specific predictive models. They divide feature spaces into regions with sparse oblique tree splitting and assign local sparse additive experts to individual regions. In order to maintain OT-SpAM interpretability, we have to keep the overall model structure simple, and this produces simultaneous model selection issues for sparse oblique region structures and sparse local experts. We address this problem by extending factorized asymptotic Bayesian inference. We demonstrate, on simulation, benchmark, and real world datasets that, in terms of accuracy, OT-SpAMs outperform state-of-the-art interpretable models and perform competitively with kernel SVMs, while still providing results that are highly understandable. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2783258.2783407 | ACM Knowledge Discovery and Data Mining |
Keywords | Field | DocType |
Interpretable Model,Model Selection,Sparseness | Kernel (linear algebra),Data mining,Interpretability,Oblique case,Bayesian inference,Additive model,Model representation,Computer science,Support vector machine,Model selection,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
10 | 0.71 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jialei Wang | 1 | 77 | 10.29 |
| Ryohei Fujimaki | 2 | 193 | 16.93 |
| Yosuke Motohashi | 3 | 14 | 2.47 |