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An Experimental Survey of Missing data Imputation Algorithms (Extended Abstract)

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An Experimental Survey of Missing Data Imputation Algorithms...

International Conference on data Engineering

作者： Xiaoye Miao Yangyang Wu Lu Chen Yunjun Gao Jianwei Yin Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Software College Zhejiang University Ningbo China College of Computer Science Zhejiang University Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle missing data problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation.

关键词： Surveys Systematics Benchmark testing Prediction algorithms data engineering Imputation data models

An empirical examination of balancing strategy for counterfactual estimation on time series 24

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An empirical examination of balancing strategy for counterfa...

Proceedings of the 41st International Conference on machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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Nonparametric inference of higher order interaction patterns in networks

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arXiv 2024年

作者： Wegner, Anatol E. Olhede, Sofia C. Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science University of Würzburg Würzburg97070 Germany Institute of Mathematics École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland

We propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs. The method is based on a class of analytically solvable generative models, where vertices are connected via explicit copies of motifs, which in combination with non-parametric priors allow us to infer higher order interactions from dyadic graph data without any prior knowledge on the types or frequencies of such interactions. Crucially, we also consider’degree–corrected’ models that correctly reflect the degree distribution of the network and consequently prove to be a better fit for many real world–networks compared to non-degree corrected models. We test the presented approach on simulated data for which we recover the set of underlying higher order interactions to a high degree of accuracy. For empirical networks the method identifies concise sets of atomic subgraphs from within thousands of candidates that cover a large fraction of edges and include higher order interactions of known structural and functional significance. The method not only produces an explicit higher order representation of the network but also a fit of the network to analytically tractable models opening new avenues for the systematic study of higher order network structures. Copyright © 2024, The Authors. All rights reserved.

关键词： machine learning

Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

Revisiting Common Randomness, No-signaling and Information Structure in Decentralized Control

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arXiv 2024年

作者： Dhingra, Apurva Kulkarni, Ankur A. Center of Machine Intelligence and Data Science CMInDS Indian Institute of Technology Bombay Mumbai400076 India Systems and Control Engineering Indian Institute of Technology Bombay Mumbai400076 India Center of excellence in Quantum Information Computing Science and Technology QuICST Indian Institute of Technology Bombay Mumbai400076 India

This work revisits the no-signaling condition for decentralized information structures. We produce examples to show that within the no-signaling polytope exist strategies that cannot be achieved by passive common randomness but instead require agents to either share their observations with a mediator or communicate directly with each other. This poses a question mark on whether the no-signaling condition truly captures the decentralized information structure in the strictest *** Codes 93C41, 93E20, 81Q93 © 2024, CC BY.

关键词： Random processes

RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation

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RETIA: Relation-Entity Twin-Interact Aggregation for Tempora...

International Conference on data Engineering

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations; therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands" problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://***/CGCL-codes/RETIA.

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Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection

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arXiv 2023年

作者： Yu, Haojun Li, Youcheng Wu, QuanLin Zhao, Ziwei Chen, Dengbo Wang, Dong Wang, Liwei National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Center of Data Science Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Yizhun Medical AI Co. Ltd Beijing China Guangdong China

During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC) [15]. To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. We release the code, checkpoints, and high-quality labels of the CVA-BUS dataset [9] in https://***/HaojunYu1998/UltraDet. © 2023, CC BY-NC-ND.

关键词： Object detection

Advancing OCT-Based Retinal Disease Classification with XLSTM: A Framework for Variable-Length Volume Processing

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Advancing OCT-Based Retinal Disease Classification with XLST...

IEEE International Symposium on Biomedical Imaging

作者： Emese Sükei Marzieh Oghbaie Ursula Schmidt-Erfurth Günter Klambauer Hrvoje Bogunović Department of Ophthalmology OPTIMA Lab Medical University of Vienna Austria Institute of Artificial Intelligence Medical University of Vienna Center for Medical Data Science Austria LIT AI Lab Institute for Machine Learning Johannes Kepler University Austria NXAI GmbH Linz Austria

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This paper presents a method for retinal disease classification using optical coherence tomography (OCT) scans, specifically addressing the challenge of variable B-scan density across dataset volumes. Deep learning methods often struggle with fluctuations in the number of B-scans within OCT volumes, which can limit their effectiveness. To address this, we propose an extended long short-term memory (xLSTM) model capable of processing variable-length B-scan sequences, capturing spatial and contextual information across entire volumes regardless of slice count. Leveraging xLSTM's advanced memory structure, our method enhances classification accuracy while maintaining computational efficiency. We evaluated the xLSTM-based approach on public and private datasets, demonstrating its robustness against inter-study variability and domain shifts. Experimental results show that this approach performs on par with or surpasses state-of-the-art baselines and offers a scalable solution for accurate 3D volume classification.

关键词： Three-dimensional displays Accuracy Optical coherence tomography Pipelines Memory management Retina Robustness Long short term memory Diseases Biomedical imaging

An Effective Meaningful Way to Evaluate Survival Models

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arXiv 2023年

作者： Qi, Shi-Ang Kumar, Neeraj Farrokh, Mahtab Sun, Weijie Kuan, Li-Hao Ranganath, Rajesh Henao, Ricardo Greiner, Russell Computing Science University of Alberta Edmonton Canada Alberta Machine Intelligence Institute Edmonton Canada Computer Science & Center for Data Science New York University New York City United States Biostatistics & Bioinformatics Duke University Durham United States

One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) – the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately, this is challenging because, in practice, the test set includes (right) censored individuals, meaning we do not know when a censored individual actually experienced the event. In this paper, we explore various metrics to estimate MAE for survival datasets that include (many) censored individuals. Moreover, we introduce a novel and effective approach for generating realistic semi-synthetic survival datasets to facilitate the evaluation of metrics. Our findings, based on the analysis of the semi-synthetic datasets, reveal that our proposed metric (MAE using pseudo-observations) is able to rank models accurately based on their performance, and often closely matches the true MAE – in particular, is better than several alternative methods. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning