Novel Tsetlin Machine Mechanisms for Logic-based Regression and Classification with Support for Continuous Input, Clause Weighting, Confidence Assessment, Deterministic Learning, and Convolution

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Multiple-Aspect Analysis of Semantic Trajectories

This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

Boosting Point-of-Interest Recommendation with Multigranular Time Representations

Technologies of recommender systems are being increasingly adopted by Location Based Social Networks (LBSNs) with the purpose of recommending Pointsof-Interest (POIs) to their users, and different contextual characteristics have been incorporated to enhance this process. Among these characteristics, the time at which users express their preferences (typically, by checking-in to different POIs) and ask for recommendations, is frequently referred as a first-order feature in this process. However, even when its influence on improving the accuracy of recommendations has been empirically demonstrated, time is still mainly considered through a monogranular representation (one-hour or one-day blocks). In this article, we introduce a POI recommendation approach based on a multigranular characterization of time, composed of hour, day-of-the-week, and month. Based on this concept, we propose two representations of user check-ins: one that directly extends a monogranular proposal of time for POI recommendations, and other based on a statistical representation of check-in distributions in time. For both representations, corresponding algorithms to compute user similarity and preference prediction are introduced. The experimental evaluation shows promising results in terms of accuracy and scalability

Text Embedding-based Event Detection for Social and News Media

Today, social and news media are the leading platforms that distribute newsworthy content, and most internet users access them regularly to get information. However, due to the data’s unstructured nature and vast volume, manual analyses to extract information require enormous effort. Thus, automated intelligent mechanisms have become crucial. The literature presents several emerging approaches for social and news media event detection, along with distinct evolutions, mainly due to the variations in the media. However, most available social media event detection approaches primarily rely on data statistics, ignoring linguistics, making them vulnerable to information loss. Also, the available news media event detection approaches mostly fail to capture long-range text dependencies and support predictions of low-resource languages (i.e. languages with relatively fewer data). The possibility of utilising interconnections between different data levels to improve final predictions also has not been adequately explored. This research investigates how the characteristics of text embeddings built using prediction-based models that have proven capabilities to capture linguistics can be used in event detection while defeating available limitations. Initially, it redefines the problem of event detection based on two data granularities, coarse- and fine-grained levels, to allow systems to tackle different information requirements. Mainly, the coarse-grained level targets the notification of event occurrences and the fine-grained level targets the provision of event details. Following the new definition, this research proposes two novel approaches for coarse- and fine-grained level event detections on social media, Embed2Detect and WhatsUp, mainly utilising linguistics captured by self-learned word embeddings and their hierarchical relationships in dendrograms. For news media event detection, this proposes a TRansformer-based Event Document classification architecture (TRED) involving long-sequence and cross-lingual transformer encoders and a novel learning strategy, Two-phase Transfer Learning (TTL), supporting the capturing of long-range dependencies and data level interconnections. All the proposed approaches have been evaluated on recent real datasets, covering four aspects crucial for event detection: accuracy, efficiency, expandability and scalability. Social media data from two diverse domains and news media data from four high- and low-resource languages are mainly involved. The obtained results reveal that the proposed approaches outperform the state-of-the-art methods despite the data diversities, proving their accuracy and expandability. Additionally, the evaluations on efficiency and scalability adequately confirm the methods’ appropriateness for (near) real-time processing and ability to handle large data volumes. In summary, the achievement of all crucial requirements evidences the potential and utility of proposed approaches for event detection in social and news media