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- W2889855722 abstract "We present Multi-value Rule Sets (MRS) for interpretable classification with feature efficient presentations. Compared to rule sets built from single-valued 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-valued 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 a MRS model and propose 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." @default.
- W2889855722 created "2018-09-27" @default.
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- W2889855722 date "2018-12-03" @default.
- W2889855722 modified "2023-10-18" @default.
- W2889855722 title "Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations" @default.
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