The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Perry Robinson Macneille - One of the best experts on this subject based on the ideXlab platform.
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a bayesian framework for learning rule sets for interpretable classification
Journal of Machine Learning Research, 2017Co-Authors: Tong Wang, Cynthia Rudin, Finale Doshivelez, Yimin Liu, Erica Klampfl, Perry Robinson MacneilleAbstract:We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If X Satisfies (Condition A AND Condition B) OR (Condition C) OR ..., then Y = 1. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets - BRS) to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.
Tong Wang - One of the best experts on this subject based on the ideXlab platform.
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a bayesian framework for learning rule sets for interpretable classification
Journal of Machine Learning Research, 2017Co-Authors: Tong Wang, Cynthia Rudin, Finale Doshivelez, Yimin Liu, Erica Klampfl, Perry Robinson MacneilleAbstract:We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If X Satisfies (Condition A AND Condition B) OR (Condition C) OR ..., then Y = 1. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets - BRS) to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.
Yang Yongbao - One of the best experts on this subject based on the ideXlab platform.
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monoids over which every flat right act Satisfies Condition p
Communications in Algebra, 1994Co-Authors: Liu Zhongkui, Yang YongbaoAbstract:(1994). Monoids over which every flat right act Satisfies Condition (P) Communications in Algebra: Vol. 22, No. 8, pp. 2861-2875.
Erica Klampfl - One of the best experts on this subject based on the ideXlab platform.
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a bayesian framework for learning rule sets for interpretable classification
Journal of Machine Learning Research, 2017Co-Authors: Tong Wang, Cynthia Rudin, Finale Doshivelez, Yimin Liu, Erica Klampfl, Perry Robinson MacneilleAbstract:We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If X Satisfies (Condition A AND Condition B) OR (Condition C) OR ..., then Y = 1. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets - BRS) to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.
Finale Doshivelez - One of the best experts on this subject based on the ideXlab platform.
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a bayesian framework for learning rule sets for interpretable classification
Journal of Machine Learning Research, 2017Co-Authors: Tong Wang, Cynthia Rudin, Finale Doshivelez, Yimin Liu, Erica Klampfl, Perry Robinson MacneilleAbstract:We present a machine learning algorithm for building classifiers that are comprised of a small number of short rules. These are restricted disjunctive normal form models. An example of a classifier of this form is as follows: If X Satisfies (Condition A AND Condition B) OR (Condition C) OR ..., then Y = 1. Models of this form have the advantage of being interpretable to human experts since they produce a set of rules that concisely describe a specific class. We present two probabilistic models with prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability. We provide a scalable MAP inference approach and develop theoretical bounds to reduce computation by iteratively pruning the search space. We apply our method (Bayesian Rule Sets - BRS) to characterize and predict user behavior with respect to in-vehicle context-aware personalized recommender systems. Our method has a major advantage over classical associative classification methods and decision trees in that it does not greedily grow the model.
