An Adaptive Framework for Confidence-constraint Rule Set Learning Algorithm in Large Dataset

Li, Meng; Yu, Lu; Zhang, Yalin; Huang, Xiaoguang; Shi, Qitao; Cui, Qing; Yang, Xinxing; Li, Longfei; Zhu, Wei; Fang, Yanming; Zhou, Jun

doi:10.1145/3511808.3557088

Cited by 4 publications

(2 citation statements)

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“…One popular approach to interpretable models is the use of decision rule sets [2], [3], [4], [5], [6], which are inherently interpretable because the rules are expressed in simple if-then sentences that correspond to logical combinations of input conditions that must be satisfied for a classification. While decision rule sets are natural classifiers, for which the performance is generally measured by the overall classification accuracy, coverage and rule precision are also commonly considered important metrics of decision rules.…”

Section: A Related Workmentioning

confidence: 99%

“…While decision rule sets are natural classifiers, for which the performance is generally measured by the overall classification accuracy, coverage and rule precision are also commonly considered important metrics of decision rules. In particular, [6] proposes to impose an additional constraint on precision to improve the performance of the rule sets. Besides decision rule sets, decision lists [7], [8] and decision trees [9] are also interpretable rule-based models.…”

Section: A Related Workmentioning

confidence: 99%

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Disjunctive Threshold Networks for Tabular Data Classification

Wang

Qiao

Lin

2023

IEEE Open J. Comput. Soc.

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While neural networks have been achieving increasingly significant excitement in solving classification tasks such as natural language processing, their lack of interpretability becomes a great challenge for neural networks to be deployed in certain high-stakes human-centered applications. To address this issue, we propose a new approach for generating interpretable predictions by inferring a simple three-layer neural network with threshold activations, so that it can benefit from effective neural network training algorithms and at the same time, produce human-understandable explanations for the results. In particular, the hidden layer neurons in the proposed model are trained with floating point weights and binary output activations. The output neuron is also trainable as a threshold logic function that implements a disjunctive operation, forming the logical-OR of the first-level threshold logic functions. This neural network can be trained using state-of-the-art training methods to achieve high prediction accuracy. An important feature of the proposed architecture is that only a simple greedy algorithm is required to provide an explanation with the prediction that is human-understandable. In comparison with other explainable decision models, our proposed approach achieves more accurate predictions on a broad set of tabular data classification datasets.

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