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Change Points Detection in Multivariate Signal Applied to Human Activity Segmentation 8th

Change Points Detection in Multivariate Signal Applied to Hu...

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8th International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Haranczyk, Grzegorz Kraków Poland

ISBN: (纸本)9783031498954;9783031498961

The detection of change points in multivariate signal without access to annotated data is a challenging task. The fully unsupervised approach requires the development of a robust algorithm that can effectively identify unknown a priori patterns. In this article we will present one of the solutions to "Human Activity Segmentation Challenge" ECML/PKDD'23 [4] where the task was to predict the offsets of activity transitions for multivariate time series. The described solution won the first place.

关键词： multivariate signal segmentation unsupervised learning change point detection (CPD) human activity recognition (HAR)

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Enhancing Table Retrieval with Dual Graph Representations

Enhancing Table Retrieval with Dual Graph Representations

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Liu, Tianyun Zhang, Xinghua Zhang, Zhenyu Wang, Yubin Li, Quangang Zhang, Shuai Liu, Tingwen Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783031434204;9783031434211

Table retrieval aims to rank candidate tables for answering natural language query, in which the most critical problem is how to learn informative representations for structured tables. Most previous methods roughly flatten the table and send it into a sequence encoder, ignoring the structure information of tables and the semantic interaction between table cells and contexts. In this paper, we propose a dual graph based method to perceive the semantics and structure of tables, so as to preferably support the downstream table retrieval task. Inspired by human cognition, we first decouple a table into the row view and column view, then build dual graphs from these two views with the consideration of table contexts. Afterward, intra-graph and inter-graph interactions are iteratively performed for aggregating and exchanging local row- and column-oriented features respectively, and an adaptive fusion strategy is eventually tailor-made for sophisticated table representations. In this way, the table structure and semantic information are well considered with dual-graph modeling. Consequently, the input query can match the target tables based on their full-fledged table representations and achieve the ultimate ranking results more accurately. Extensive experiments verify the superiority of our dual graphs over strong baselines on two table retrieval datasetsWikiTables andWebQueryTable. Further analyses also confirm the adaptability for row-/column-oriented tables, and show the rationality and generalization of dual graphs. The source code is available at https://***/ty33123/DualG.

关键词： table understanding table retrieval graph representation learning

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Exploiting Phrase Interrelations in Span-level Neural Approaches for Aspect Sentiment Triplet Extraction 1

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Naglik, Iwo Lango, Mateusz Poznan Univ Tech Inst Comp Sci Poznan Poland

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in modern natural language processing concerning the automatic extraction of (aspect phrase, opinion phrase, sentiment polarity) triplets from a given text. Current state-of-the-art methods achieve relatively high results by analyzing all possible spans extracted from a text. Due to a high number of analyzed spans, span-level methods usually apply some kind of pruning operators that interrupt the gradient flow. They also do not analyze interrelations between spans while constructing model output, relying on independent, sequential predictions for candidate triplets. This paper presents a new span-level approach that applies a learnable extractor of spans and a differentiable span selector that enables end2end training. The approach relies on a fully connected pairwise CRF model to capture interrelations between spans while constructing the output. Conducted experiments demonstrated that the proposed approach achieves superior results in terms of F1-score in comparison to other, state-of-the-art ASTE methods.

关键词： aspect sentiment triplet extraction span-level approaches sentiment analysis natural language processing deep learning

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ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction

ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Ex...

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Jain, Monika Singh, Kuldeep Mutharaju, Raghava IIIT Delhi Knowledgeable Comp & Reasoning KRaCR Lab Delhi India Cerence GmbH & Zerotha Res Aachen Germany

ISBN: (纸本)9783031434204;9783031434211

Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary, which is generally in the form of a Knowledge Graph (KG) or an ontology. Various approaches have been proposed so far to address this task. However, applying these techniques to biomedical text often yields unsatisfactory results because it is hard to infer relations directly from sentences due to the nature of the biomedical relations. To address these issues, we present a novel technique called ReOnto, that makes use of neuro symbolic knowledge for the RE task. ReOnto employs a graph neural network to acquire the sentence representation and leverages publicly accessible ontologies as prior knowledge to identify the sentential relation between two entities. The approach involves extracting the relation path between the two entities from the ontology. We evaluate the effect of using symbolic knowledge from ontologies with graph neural networks. Experimental results on two public biomedical datasets, BioRel and ADE, show that our method outperforms all the baselines (approximately by 3%).

关键词： ontology neuro-symbolic integration graph neural network domain knowledge relation extraction

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Semi-Supervised Text Classification via Self-Paced Semantic-Level Contrast 27th

Semi-Supervised Text Classification via Self-Paced Semantic-...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Xia, Yu Zhang, Kai Zhou, Kaijie Wang, Rui Hui, Xiaohui Chinese Acad Sci Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Realsee Beijing Peoples R China Univ Liverpool Liverpool Merseyside England

ISBN: (纸本)9783031333767;9783031333774

Semi-Supervised Text Classification (SSTC) aims to explore discriminative information from unlabeled texts in a self-training manner. These methods pre-train the deep classifier on labeled texts. Recent works further fine-tune the model on the combination of labeled texts and pseudo-labeled texts generated by the pre-trained deep classifier. However, the model's performance largely depends on the quality of pseudo-labels. To tackle such an issue, we propose a novel approach, namely Self-paced Semantic-level Contrastive Learning ((SCL)-C-2) for SSTC. (SCL)-C-2 imposes a self-paced pseudo-label generator to improve the quality of pseudo-labels. We innovatively propose robust supervised learning and semantic-level contrastive learning modules to alleviate the model's over-sensitivity to pseudo-labels' quality. Empirically, (SCL)-C-2 significantly outperforms the state-of-the-art methods on benchmark datasets with 0.3% - 4.6% improvements on Micro-F1 and 0.3% - 11.1% improvements on Macro-F1. Furthermore, we establish a practical dataset, i.e., Events39, to provide a benchmark for evaluating the robustness against domain-shift of SSTC methods. The experimental results demonstrate the effectiveness of (SCL)-C-2 on Events39.

关键词： Semi-supervised learning Contrastive learning Text classification

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Synthetic Data for Feature Selection 22nd

Synthetic Data for Feature Selection

引用

22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Kamalov, Firuz Sulieman, Hana Cherukuri, Aswani Kumar Canadian Univ Dubai Dept Elect Engn Dubai U Arab Emirates Amer Univ Sharjah Dept Math & Stat Sharjah U Arab Emirates Vellore Inst Technol Sch IT & Engn Vellore Tamil Nadu India

ISBN: (纸本)9783031425073;9783031425080

Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection algorithms. Synthetic datasets allow for precise evaluation of selected features and control of the data parameters for comprehensive assessment. The proposed datasets are based on applications from electronics in order to mimic real life scenarios. To illustrate the utility of the proposed data we employ one of the datasets to test several popular feature selection algorithms. The datasets are made publicly available on GitHub and can be used by researchers to evaluate feature selection algorithms.

关键词： feature selection synthetic data electronics

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Spatial-Temporal Diffusion Probabilistic Learning for Crime Prediction 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Gao, Qiang Fu, Hongzhu Wei, Yutao Huang, Li Liu, Xingmin Liu, Guisong Southwestern Univ Finance & Econ Complex Lab New Finance & Econ Chengdu Sichuan Peoples R China Kash Inst Elect & Informat Ind Kashgar Xinjiang Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (数字)9783031402869

ISBN: (纸本)9783031402852;9783031402869

Crime prediction is a critical issue in the field of urban management, as it makes it possible to prevent potential public safety threats. Previous studies mainly concentrate on exploring the multiple dependencies regarding urban regions and temporal interactions. Nevertheless, more complex interactive semantics behind crime events remain largely unexplored. Besides, the sparsity and uncertainty of historical crime data have not been thoroughly investigated. In this study, we introduce a novel spatial-temporal diffusion probabilistic learning framework for crime prediction, namely ST-DPL. To be specific, we devise a spatial-temporal crime encoding module in ST-DPL to handle multiple dependencies covering the temporal, spatial, and crime-type aspects. Then, the designed crime diffusion probabilistic (CDP) module in ST-DPL plays the role of generating a more robust behavioral portrayal regarding historical crime cases for future crime prediction. The experiments conducted on two real-world datasets demonstrate that ST-DPL outperforms the state-of-the-art baselines.

关键词： crime prediction temporal dependency spatial dependency diffusion model attentive network

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Combination of Deep Learning and Ambiguity Rejection for Improving Image-Based Disease Diagnosis 15th

Combination of Deep Learning and Ambiguity Rejection for Imp...

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15th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Thanh-An Pham Van-Dung Hoang Ho Chi Minh Univ Banking Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol & Educ Ho Chi Minh City Vietnam

ISBN: (纸本)9789819958337;9789819958344

artificial intelligent (AI) based medical image recognition plays important task to assist in many disease diagnosis systems. In medical diagnosis, the incorrect decision is very serious. The healthcare diagnosis guides the treatment plan, and it is significant impact on the patient's health outcomes. An incorrect diagnosis can lead to delays in treatment or even the wrong treatment being administered, which results in serious harm to the patient. In this article, we propose an approach to reject ambiguity samples in the classification results, which improve the accuracy of the medical image- based diseases diagnose. In this study, we also experimented using some well-known deep learning models such as MobileNet (lightweight architecture) and DenseNet (more complex and dense connected architecture). Additionally, we combine with some solutions to address the problem of the data imbalance such as focal loss and data augmentation techniques. In the classification stage, there are still significant misclassification results. Therefore, we present the solution for ambiguity rejection of uncertain samples. Experimental results show that the accuracy increases significantly after removing uncertain samples. The high removal rate of uncertain samples also affects to the diagnosing quality. This approach eliminates uncertain samples, which utilizes for improving the diagnosing quality from results of deep learning classification around 10% recall and 70% coverage rate, respectively.

关键词： Ambiguity rejection Classification Feature extraction Medical image processing

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DualMatch: Robust Semi-supervised Learning with Dual-Level Interaction

DualMatch: Robust Semi-supervised Learning with Dual-Level I...

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Wang, Cong Cao, Xiaofeng Guo, Lanzhe Shi, Zenglin Jilin Univ Sch Artificial Intelligence Changchun 130012 Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China ASTAR I2R Singapore Singapore

ISBN: (纸本)9783031434235;9783031434242

Semi-supervised learning provides an expressive framework for exploiting unlabeled data when labels are insufficient. Previous semi-supervised learning methods typically match model predictions of different data-augmented views in a single-level interaction manner, which highly relies on the quality of pseudo-labels and results in semi-supervised learning not robust. In this paper, we propose a novel SSL method called Dual-Match, in which the class prediction jointly invokes feature embedding in a dual-level interaction manner. Dual-Match requires consistent regularizations for data augmentation, specifically, 1) ensuring that different augmented views are regulated with consistent class predictions, and 2) ensuring that different data of one class are regulated with similar feature embeddings. Extensive experiments demonstrate the effectiveness of DualMatch. In the standard SSL setting, the proposal achieves 9% error reduction compared with SOTA methods, even in a more challenging class-imbalanced setting, the proposal can still achieve 6% error reduction. Code is available at https://***/CWangAI/DualMatch.

关键词： Semi-supervised learning Dual-Level interaction Data augmentation Feature embeddings

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Multiple-Source Adaptation Using Variational Renyi Bound Optimization

Multiple-Source Adaptation Using Variational Renyi Bound Opt...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Zalman (Oshri), Dana Fine, Shai Reichman Univ Sch Comp Sci Herzliyya Israel Reichman Univ Data Sci Inst Herzliyya Israel

ISBN: (纸本)9783031434235;9783031434242

Multiple Source Adaptation (MSA) is a problem that involves identifying a predictor which minimizes the error for the target domain while utilizing the predictors from the source domains. In practice, the source domains typically exhibit varying probability distributions across the input space and are unknown to the learner. Consequently, accurate probability estimates are essential for effectively addressing the MSA problem. To this end, variation inference is an attractive approach that aims to approximate probability densities. Traditionally, it is done by maximizing a lower bound for the likelihood of the observed data (evidence), i.e. maximizing the Evidence Lower BOund (ELBO). Recently, researchers have proposed optimizing the Variational Renyi bound (VR) instead of ELBO, which can be biased or difficult to approximate due to high variance. To address these issues, we propose a new upper bound called Variational Renyi Log Upper bound (VRLU). Unlike existing VR bounds, the VRLU bound maintains the upper bound property when using the Monte Carlo (MC) approximation. Additionally, we introduce the Variational Renyi Sandwich (VRS) method, which jointly optimizes an upper and a lower bound, resulting in a more accurate density estimate. Following this, we apply the VRS density estimate to the MSA problem. We show, both theoretically and empirically, that using VRS estimators provides tighter error bounds and improved performance, compared to leading MSA methods.

关键词： Multiple-Source Adaptation Variational Inference Renyi Divergence

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