Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Tu:2020:CEC,

• author = "Chaofan Tu and Menglin Cui",
• booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing Approach",
• year = "2020",
• editor = "Yaochu Jin",
• month = "19-24 " # jul,
• keywords = "genetic algorithms, genetic programming, Medical diagnostic imaging, Simulated annealing, Task analysis, Neural networks, Machine learning, Medical services, Knowledge engineering, simulated annealing, regular expression, medical text classification",
• isbn13 = "978-1-7281-6929-3",
• DOI = "doi:10.1109/CEC48606.2020.9185650",
• abstract = "In this paper, we propose a rule-based engine composed of high-quality and interpretable regular expressions for medical text classification. The regular expressions are autogenerated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present high quality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable {"}black boxes{"} to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labour-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions. The Pool-based Simulated Annealing method is proposed to automatically optimize the performance of machine-generated regular expressions without human interference. The proposed method is tested on real-life data provided by one of China's largest online medical platforms. Experimental results show that the proposed PSA method further improves the performance of initial machine-generated regular expressions compared with other meta-heuristics such as Genetic Programming. We also believe that the proposed method can serve as a vital complementary tool for the existing machine learning approaches in text classification applications when high levels of interpretability of the solutions are required.",
• notes = "Also known as \cite{9185650}",

}

Genetic Programming entries for Chaofan Tu Menglin Cui

Citations