Bimal Bhattarai

• Bhattarai, Bimal; Granmo, Ole-Christoffer; Lei, Jiao; Andersen, Per-Arne; Tunheim, Svein Anders & Shafik, Rishad Ahmed [Show all 7 contributors for this article] (2023). Contracting Tsetlin Machine with Absorbing Automata, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10455040.
• Przybysz, Emilia Katarzyna; Bhattarai, Bimal; Persia, Cosimo Damiano; Ozaki, Ana; Granmo, Ole-Christoffer & Sharma, Jivitesh (2023). Verifying Properties of Tsetlin Machines, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE conference proceedings. ISSN 979-8-3503-4477-6. doi: 10.1109/ISTM58889.2023.10454997.
• Bhattarai, Bimal; Saha, Rupsa; Granmo, Ole-Christoffer; Zadorozhny, Vladimir & Xu, Jiawei (2023). A Logic-Based Explainable Framework for Relation Classification of Human Rights Violations. CEUR Workshop Proceedings. ISSN 1613-0073. 3464, p. 14–21. Show summary

  Using a Relational Tsetlin Machine (RTM) for analysis of semi-structured data allows the use of inherent relational structures present in natural language text to get an explainable classification of data. A finite Herbrand model derives Horn Clauses from the model, which are simple yet powerful logical tools that can build an abstract view of the world. We use the same to analyze human rights violation data. We show concretely how natural language can be transformed into a relational structure, and further use the Relational Tsetlin Machine to not only classify incidents as serious and non-serious violations but also explore the patterns learned by the RTM in order to arrive that those decisions. Furthermore, the distilled Horn Clauses show a precise understanding of the concepts involved without the drawback of textual ambiguity.

• Abeyrathna, Kuruge Darshana; Abouzeid, Ahmed Abdulrahem Othman; Bhattarai, Bimal; Giri, Charul; Glimsdal, Sondre & Granmo, Ole-Christoffer [Show all 11 contributors for this article] (2023). Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size . In Elkind, Edith (Eds.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AAAI Press. ISSN 978-1-956792-03-4. p. 3395–3403. doi: 10.24963/ijcai.2023/378. Full text in Research Archive Show summary

  Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning – Clause Size Constrained TMs (CSC-TMs) – where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classifcation, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We fnally analyze CSC-TM power consumption and derive new convergence properties.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2023). An Interpretable Knowledge Representation Framework for Natural Language Processing with Cross-Domain Application, Advances in Information Retrieval. ECIR 2023. Springer. ISSN 978-3-031-28244-7. p. 167–181. doi: 10.1007/978-3-031-28244-7_11. Full text in Research Archive Show summary

  Data representation plays a crucial role in natural language processing (NLP), forming the foundation for most NLP tasks. Indeed, NLP performance highly depends upon the effectiveness of the preprocessing pipeline that builds the data representation. Many representation learning frameworks, such as Word2Vec, encode input data based on local contextual information that interconnects words. Such approaches can be computationally intensive, and their encoding is hard to explain. We here propose an interpretable representation learning framework utilizing Tsetlin Machine (TM). The TM is an interpretable logic-based algorithm that has exhibited competitive performance in numerous NLP tasks. We employ the TM clauses to build a sparse propositional (boolean) representation of natural language text. Each clause is a class-specific propositional rule that links words semantically and contextually. Through visualization, we illustrate how the resulting data representation provides semantically more distinct features, better separating the underlying classes. As a result, the following classification task becomes less demanding, benefiting simple machine learning classifiers such as Support Vector Machine (SVM). We evaluate our approach using six NLP classification tasks and twelve domain adaptation tasks. Our main finding is that the accuracy of our proposed technique significantly outperforms the vanilla TM, approaching the competitive accuracy of deep neural network (DNN) baselines. Furthermore, we present a case study showing how the representations derived from our framework are interpretable. (We use an asynchronous and parallel version of Tsetlin Machine: available at https://github.com/cair/PyTsetlinMachineCUDA).

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment . In Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Ed.), Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. p. 4894–4903. Full text in Research Archive Show summary

  The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least 5% in terms of accuracy, with the added benefit of an interpretable logic-based representation. In addition, our approach provides a higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model’s explainability, demonstrating how it decomposes into meaningful words and their negations.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification. In Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Ed.), Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. p. 3761–3770. Show summary

  Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and decision-making. Tsetlin Machine (TM) employs human-interpretable conjunctive clauses in propositional logic to solve complex pattern recognition problems and has demonstrated competitive performance in various NLP tasks. In this paper, we propose ConvTextTM, a novel convolutional TM architecture for text classification. While legacy TM solutions treat the whole text as a corpus-specific set-of-words (SOW), ConvTextTM breaks down the text into a sequence of text fragments. The convolution over the text fragments opens up for local position-aware analysis. Further, ConvTextTM eliminates the dependency on a corpus-specific vocabulary. Instead, it employs a generic SOW formed by the tokenization scheme of the Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2019a). The convolution binds together the tokens, allowing ConvTextTM to address the out-of-vocabulary problem as well as spelling errors. We investigate the local explainability of our proposed method using clause-based features. Extensive experiments are conducted on seven datasets, to demonstrate that the accuracy of ConvTextTM is either superior or comparable to state-of-the-art baselines.

• Jenul, Anna Selina; Bhattarai, Bimal; Liland, Kristian Hovde; Lei, Jiao; Schrunner, Stefan & Futsæther, Cecilia Marie [Show all 8 contributors for this article] (2022). Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling. In Shafik, Rishad (Eds.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. p. 57–64. doi: 10.1109/ISTM54910.2022.00019. Show summary

  Tabular data with few observations and many features are common in the healthcare domain. With its rule-based approach to data modelling, the Tsetlin Machine has considerable potential to be a valuable tool in healthcare data analysis by providing interpretability to medical personnel. However, the performance of Tsetlin Machine models may be hampered by the presence of noise, which may often be the case with healthcare data due to individual differences across patients. This study shows that intelligent pre-filtering of healthcare measurement data using so-called component-based methods, such as Principal Component Analysis or Partial Least Squares Regression, can be beneficial for the performance of Tsetlin Machines. Modelling four healthcare data sets shows that the Tsetlin Machine achieved better predictive performance on pre-filtered data for data sets with high features-to-observations ratios.

• Glimsdal, Sondre; Saha, Rupsa; Bhattarai, Bimal; Giri, Charul; Sharma, Jivitesh & Tunheim, Svein Anders [Show all 7 contributors for this article] (2022). Focused Negative Sampling for Increased Discriminative Power in Tsetlin Machines. In Shafik, Rishad (Eds.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE conference proceedings. ISSN 978-1-6654-7116-9. p. 73–80. doi: 10.1109/ISTM54910.2022.00021. Show summary

  Tsetlin Machines learn from input data by creating patterns in propositional logical, using the literals available in the data. These patterns vote for the classes in a classification task. Despite their simplistic premise, Tsetlin machine (TM)s have been performing at with other popular machine learning methods across various benchmarks. Not only accuracy, TMs also perform well in terms of energy efficiency and learning speed. The general TM scheme works best when there is sufficient discriminatory information available between two classes. In this paper, we explore the use of focused negative sampling (FNS) to discriminate between classes which are not easily distinguishable from each other. We carry out experiments across diverse classification tasks ranging over natural language processing, image processing, reinforcement learning to show that this approach forces the TM to arrive at patterns that can successfully tell apart two classes that are correlated. Further, we show that the proposed method achieves accuracy comparable to a vanilla Tsetlin Machine approach but in approximately 42% less number of epochs on average

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines. Applied intelligence (Boston). ISSN 0924-669X. doi: 10.1007/s10489-022-03281-1. Full text in Research Archive Show summary

  Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to making documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable phrases, successfully measuring novelty.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Jiao, Lei (2021). A Tsetlin Machine Framework for Universal Outlier and Novelty Detection, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 978-989-758-484-8. doi: 10.1007/978-3-031-10161-8_14.
• Abeyrathna, Kuruge Darshana; Bhattarai, Bimal; Goodwin, Morten; Gorji, Saeed Rahimi; Granmo, Ole-Christoffer & Lei, Jiao [Show all 8 contributors for this article] (2021). Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling. Proceedings of Machine Learning Research (PMLR). ISSN 2640-3498. Full text in Research Archive Show summary

  Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the evaluation of clauses is fast, being based on binary operators, the voting makes it necessary to synchronize the clause evaluation, impeding parallelization. In this paper, we propose a novel scheme for desynchronizing the evaluation of clauses, eliminating the voting bottleneck. In brief, every clause runs in its own thread for massive native parallelism. For each training example, we keep track of the class votes obtained from the clauses in local voting tallies. The local voting tallies allow us to detach the processing of each clause from the rest of the clauses, supporting decentralized learning. This means that the TM most of the time will operate on outdated voting tallies. We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy. Furthermore, we show that the approach provides up to 50 times faster learning. Finally, learning time is almost constant for reasonable clause amounts (employing from 20 to 7,000 clauses on a Tesla V100 GPU). For sufficiently large clause numbers, computation time increases approximately proportionally. Our parallel and asynchronous architecture thus allows processing of more massive datasets and operating with more clauses for higher accuracy.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Jiao, Lei (2021). Measuring the Novelty of Natural Language Text using the Conjunctive Clauses of a Tsetlin Machine Text Classifier, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 978-989-758-484-8. p. 410–417. doi: 10.5220/0010382204100417.
• Yadav, Rohan Kumar; Bhattarai, Bimal; Lei, Jiao; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Indoor Space Classification Using Cascaded LSTM. IEEE Conference on Industrial Electronics and Applications. ISSN 2158-2297. p. 1110–1114. doi: 10.1109/ICIEA48937.2020.9248347. Full text in Research Archive Show summary

  Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detection, and Ultra Wide Band (UWB) for indoor space classification purposes. Many of the existing approaches need extensive pre-installed infrastructure, making the cost higher to obtain reasonable accuracy. Therefore, improvements are still required to increase the accuracy with minimum requirements of infrastructure. In this paper, we propose an approach to classify the indoor space using geomagnetic field (GMF) and radio signal strength (RSS) as the identity. The indoor space is an open big test bed divided into different indiscernible subspace. We collect GMF and RSS at each subspace and classify it using cascaded Long Short Term Memory (LSTM). The experimental results show that the accuracy is significantly improved when GMF and RSS are combined to make distinct features. In addition, we compare the performance of the proposed model with the state-of-the-art machine learning methods.

• Yadav, Rohan Kumar; Bhattarai, Bimal; Gang, Hui-Seon & Pyun, Jae-Young (2019). Trusted K Nearest Bayesian Estimation for Indoor Positioning System. IEEE Access. ISSN 2169-3536. 7, p. 51484–51498. doi: 10.1109/ACCESS.2019.2910314.
• Bhattarai, Bimal; Yadav, Rohan Kumar; Gang, Hui-Seon & Pyun, Jae-Young (2019). Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning. IEEE Access. ISSN 2169-3536. doi: 10.1109/ACCESS.2019.2902573.

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• Przybysz, Emilia; Bhattarai, Bimal; Persia, Cosimo Damiano; Ozaki, Ana; Granmo, Ole-Christoffer & Sharma, Jivitesh (2023). Verifying Properties of Tsetlin Machines.
• Bhattarai, Bimal & Granmo, Ole-Christoffer (2022). Tsetlin Machine Applications.
• Granmo, Ole-Christoffer & Bhattarai, Bimal (2022). Tsetlin Machine – efficient AI technology for language processing.

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