Rule extraction from autoencoder-based connectionist computational models
Rule extraction from autoencoder-based connectionist computational models
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从基于自动编码器的联结主义计算模型中提取规则
DOI：
10.1002/cpe.4262
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发表时间：
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通讯作者：
Michael S. C. Thomas
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作者：
杨娟;Michael S. C. Thomas
映射问题是与自然语言学习相关的典型研究课题，不仅包括分类映射，还包括非分类映射，如动词及其过去时。连接主义计算模型是模拟这些映射问题的最流行的方法之一，然而，它们的解释能力的缺乏阻碍了它们被进一步用来理解语言学习过程。因此，从连接主义模型中提取合理规则的工作与模拟映射行为一样重要。不幸的是，没有可用的技术可以直接应用于这些计算模型来模拟非分类问题。针对非分类映射问题，提出了一种基于自动编码器的连接主义计算模型，为非分类映射问题提供了一种规则提取方法，该方法能够构造高保真的IF-THEN有理关系。为了证明其普适性，该计算模型被扩展到一个改进的版本，以解决与认知风格预测相关的多标签分类映射问题。实验比较了该计算模型与经典连接主义计算模型(多层感知器人工神经网络)的保真度性能，并与其他规则归纳技术的预测精度进行了比较，证明了该计算模型对非分类问题的模拟能力和解释能力。
Mapping problems are typical research topics related to natural language learning, and they include not only classification mappings but also nonclassification mappings, such as verbs and their past tenses. Connectionist computational models are one of the most popular approaches for simulating those mapping problems; however, their lack of explanatory ability has prevented them from being further used to understand the language learning process. Therefore, the work of extracting rational rules from a connectionist model is as important as simulating the mapping behaviors. Unfortunately, there is no available technique that can be directly applied in those computational models to simulate nonclassification problems. In this paper, an autoencoder‐based connectionist computational model is proposed to derive a rule extraction method that can construct “If‐Then” rational relations with high fidelity for nonclassification mapping problems. To demonstrate its generalizability, this computational model is extended to a modified version to address a multi‐label classification mapping problem related to cognitive style prediction. Experiments prove this computational model's simulation ability and its explanatory ability on nonclassification problems by comparing its fidelity performances with those of the classical connectionist computational model (multilayer perceptron artificial neural network), and its similar ability on a multi‐label classification problem (Felder‐Silverman learning style classification) by comparing its prediction accuracy with those of other rule induction techniques.