Patent
Distributed rule-based probabilistic time-series classifier
Hodjat Babak,Shahrzad Hormoz +1 more
- 12 Oct 2017
1
TL;DR: In this paper, a genetic algorithm is used to derive a ruleset which predicts the probability of a particular outcome, and the rule probabilities are combined with the rule-level certainty values to derive the probability output for the ruleset, which can be used to provide a basis for decisions.
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Abstract: In many environments, rules are trained on historical data to predict an outcome likely to be associated with new data. Described is a ruleset which predicts the probability of a particular outcome. Roughly described, an individual identifies a ruleset, where each of the rules has a plurality of conditions and also indicates a rule-level probability of a predetermined classification. The conditions indicate a relationship (e.g. ‘<’ or ‘!<’) between an input feature and a corresponding value. The rules are evaluated against input data to derive a certainty for each condition, and aggregated to a rule-level certainty. The rule probabilities are combined using the rule-level certainty values to derive a probability output for the ruleset, which can be used to provide a basis for decisions. In an embodiment, the per-condition certainty values are fuzzy values aggregated by fuzzy logic. A novel genetic algorithm can be used to derive the ruleset.
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Citations
Patent
Incorporating change diagnosis using probabilistic tensor regression model for improving processing of materials
Ide Tsuyoshi
- 23 Apr 2020
TL;DR: In this paper, a probability distribution of a manufacturing system's performance conditioned on a training dataset comprising a historical tensor and associated performance metric of a reference period is learned, and an overall change is determined between the training dataset's relationship of the historical tensors and associated metric.
2
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