The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

[1]  Xuan Zhang,et al.  A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks , 2019, IEA/AIE.

[2]  Alex Alves Freitas,et al.  On the Importance of Comprehensible Classification Models for Protein Function Prediction , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[3]  Leonard Kleinrock,et al.  Using Finite State Automata to Produce Self-Optimization and Self-Control , 1996, IEEE Trans. Parallel Distributed Syst..

[4]  B. John Oommen,et al.  A Solution to the Stochastic Point Location Problem in Metalevel Nonstationary Environments , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Zhiyong Lu,et al.  Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations , 2004, Nucleic Acids Res..

[6]  Yi-Cheng Liu,et al.  Using mixture design and neural networks to build stock selection decision support systems , 2017, Neural Computing and Applications.

[7]  Bart Baesens,et al.  Domain knowledge integration in data mining using decision tables: case studies in churn prediction , 2009, J. Oper. Res. Soc..

[8]  Blaz Zupan,et al.  Predictive data mining in clinical medicine: Current issues and guidelines , 2008, Int. J. Medical Informatics.

[9]  Bart Baesens,et al.  Building comprehensible customer churn prediction models with advanced rule induction techniques , 2011, Expert Syst. Appl..

[10]  Kuruge Darshana Abeyrathna,et al.  A Novel Tsetlin Automata Scheme to Forecast Dengue Outbreaks in the Philippines , 2018, 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI).

[11]  Alex Alves Freitas,et al.  Comprehensible classification models: a position paper , 2014, SKDD.

[12]  Ole-Christoffer Granmo,et al.  The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic , 2018, ArXiv.

[13]  I-Cheng Yeh,et al.  Building real estate valuation models with comparative approach through case-based reasoning , 2018, Appl. Soft Comput..

[14]  Bart Baesens,et al.  An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models , 2011, Decis. Support Syst..

[15]  Athanasios Tsanas,et al.  Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools , 2012 .

[16]  Bart Baesens,et al.  Building Intelligent Credit Scoring Systems Using Decision Tables , 2003, ICEIS.

[17]  Algirdas Avizienis,et al.  Position Paper , 1994, EDCC.

[18]  B. John Oommen,et al.  Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata , 2010, IEEE Transactions on Computers.

[19]  Kuruge Darshana Abeyrathna,et al.  The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems , 2019, EPIA.

[20]  Ole-Christoffer Granmo,et al.  Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications , 2018, IEEE Access.

[21]  Ole-Christoffer Granmo,et al.  Stochastic Learning for SAT- Encoded Graph Coloring Problems , 2010, Int. J. Appl. Metaheuristic Comput..

[22]  M J Pazzani,et al.  Acceptance of Rules Generated by Machine Learning among Medical Experts , 2001, Methods of Information in Medicine.