Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Berge, Geir Thore; Granmo, Ole‐Christoffer; Tveit, Tor Oddbjørn; Goodwin, Morten; Jiao, Lei; Matheussen, Bernt Viggo

doi:10.48550/arxiv.1809.04547

Cited by 3 publications

(3 citation statements)

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“…We must also stress that this selection is highly limited in scope and much more sophisticated and robust techniques for the denoising of images exist depending on the image acquisition method and content. It may be that making a model of the noise via a deep neural net such as UNet [56], CAIR [57], Noise2Noise or Noise2Void [58] may be required or produce better results. Any method may of course be employed before using this segmentation GUI.…”

Section: Noise Reductionmentioning

confidence: 99%

A semi-automated finite difference mesh creation method for use with immersed boundary software IB2d and IBAMR

et al. 2020

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Numerous fluid-structure interaction problems in biology have been investigated using the immersed boundary method. The advantage of this method is that complex geometries, e.g., internal or external morphology, can easily be handled without the need to generate matching grids for both the fluid and the structure. Consequently, the difficulty of modeling the structure lies often in discretizing the boundary of the complex geometry (morphology). Both commercial and open source mesh generators for finite element methods have long been established; however, the traditional immersed boundary method is based on a finite difference discretization of the structure. Here we present a software library for obtaining finite difference discretizations of boundaries for direct use in the 2D immersed boundary method. This library provides tools for extracting such boundaries as discrete mesh points from digital images. We give several examples of how the method can be applied that include passing flow through the veins of insect wings, within lymphatic capillaries, and around starfish using open-source immersed boundary software.

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Section: Noise Reductionmentioning

confidence: 99%

A semi-automated finite difference mesh creation method for use with immersed boundary software IB2d and IBAMR

et al. 2020

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“…This gives the TM an inherent computational advantage. Experimental results show that TM outperforms ANNs, Support Vector Machines (SVMs), the Naïve Bayes Classifier (NBC), Random Forests (RF), and Logistic Regression (LR) in diverse benchmarks [1,3]. These promising properties and results make the TM an interesting target for further research.…”

Section: Introductionmentioning

confidence: 99%

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

Abeyrathna¹,

Granmo²,

Zhang³

et al. 2019

Preprint

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In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed using an artificial dataset. The TM is further applied to forecast dengue outbreaks of all the seventeen regions in Philippines using the spatiotemporal properties of the data. Experimental results show that dengue outbreak forecasts made by the TM are more accurate than those obtained by a Support Vector Machine (SVM), Decision Trees (DTs), and several multi-layered Artificial Neural Networks (ANNs), both in terms of forecasting precision and F1-score.

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“…Lately, the TM has provided state-of-the-art performance in several real-life applications. Berge et al have for instance successfully used the TM for medical text categorization [8]. They used the TM to provide interpretable pattern recognition for the analysis of electronic health records.…”

Section: Introductionmentioning

confidence: 99%

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

Abeyrathna¹,

Granmo²,

Jiao³

et al. 2019

Preprint

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The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern classification by introducing a new type of TMs, namely, the Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference mechanism of the TM so that input patterns are transformed into a single continuous output, rather than to distinct categories. We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns;(2) mapping these patterns to a continuous output through a novel voting and normalization mechanism; and (3) employing a feedback scheme that updates the TM clauses to minimize the regression error. The feedback scheme uses a new activation probability function that stabilizes the updating of clauses, while the overall system converges towards an accurate input-output mapping. The performance of the RTM is evaluated using six different artificial datasets with and without noise, in comparison with the Classic Tsetlin Machine (CTM) and the Multiclass Tsetlin Machine (MTM). Our empirical results indicate that the RTM obtains the best training and testing results for both noisy and noisefree datasets, with a smaller number of clauses. This, in turn, translates to higher regression accuracy, using significantly less computational resources.

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Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Cited by 3 publications

References 0 publications

A semi-automated finite difference mesh creation method for use with immersed boundary software IB2d and IBAMR

A semi-automated finite difference mesh creation method for use with immersed boundary software IB2d and IBAMR

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

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