检索结果-内蒙古大学图书馆

CC-HIT: Creating Counterfactuals from High-Impact Transitions

CC-HIT: Creating Counterfactuals from High-Impact Transitio...

International Workshops which were held in conjunction with the 6th International Conference on Process mining, ICPM 2024

作者： Xian, Zhicong Zellner, Ludwig Tavares, Gabriel Marques Seidl, Thomas Database Systems and Data Mining LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany

ISBN: (纸本)9783031822247

Smooth process execution relies on high-quality insights extracted from event data. For instance, trace durations heavily affect performance and increase resource consumption. While many predictive systems aim to identify these inefficiencies, they often focus on individual process instances, missing the global perspective. It is essential to detect where delays occur globally and pinpoint specific activity transitions causing them. To address this, we propose CC-HIT (Creating Counterfactuals from High-Impact Transitions), which identifies temporal activity dependencies across the process. CC-HIT uses a modified game theoretic approach and counterfactual information to generate reference event logs to estimate the consequences of activity transitions. It highlights key activity transitions impacting process performance, offering actionable insights for optimization. Validation on the BPIC 2020 dataset demonstrates its effectiveness over baseline methods. © The Author(s) 2025.

关键词： Counterfactuals Impact Ranking Local Explanation Shapley Value

来源：评论

学校读者我要写书评

暂无评论

Motif Discovery Framework for Psychiatric EEG data Classification

arXiv

引用

arXiv 2025年

作者： Kraljevska, Melanija Hlavackova-Schindler, Katerina Miklautz, Lukas Plant, Claudia Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Währingerstrasse 29 Vienna1090 Austria ds:UniVie University of Vienna Austria

In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general. © 2025, CC BY.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Cause or Trigger? From Philosophy to Causal Modeling

arXiv

引用

arXiv 2025年

作者： Hlaváčková-Schindler, Kateřina Wöß, Rainer Pecorino, Vera Schindler, Philip Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria Department of Physics and Astronomy University of Catania Catania Italy Faculty of Philosophy University of Vienna Vienna Austria

Not much has been written about the role of triggers in the literature on causal reasoning, causal modeling, or philosophy. In this paper, we focus on describing triggers and causes in the metaphysical sense and on characterizations that differentiate them from each other. We carry out a philosophical analysis of these differences. From this, we formulate a definition that clearly differentiates triggers from causes and can be used for causal reasoning in natural sciences. We propose a mathematical model and the Cause- Trigger algorithm, which, based on given data to observable processes, is able to determine whether a process is a cause or a trigger of an effect. The possibility to distinguish triggers from causes directly from data makes the algorithm a useful tool in natural sciences using observational data, but also for real-world scenarios. For example, knowing the processes that trigger causes of a tropical storm could give politicians time to develop actions such as evacuation the population. Similarly, knowing the triggers of processes that cause global warming could help politicians focus on effective actions. We demonstrate our algorithm on the climatological data of two recent cyclones, Freddy and Zazu. The Cause-Trigger algorithm detects processes that trigger high wind speed in both storms during their cyclogenesis. The findings obtained agree with expert knowledge. © 2025, CC BY.

关键词： Physical properties

来源：评论

学校读者我要写书评

暂无评论

I Want’Em All (At Once) – Ultrametric Cluster Hierarchies

arXiv

引用

arXiv 2025年

作者： Draganov, Andrew Weber, Pascal Jørgensen, Rasmus Skibdahl Melanchton Beer, Anna Plant, Claudia Assent, Ira Department of Computer Science Aarhus University Aarhus Denmark Data Mining and Machine Learning University of Vienna Vienna Austria UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria

Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen. This paper generalizes these ideas by proving that, for any reasonable hierarchy, one can optimally solve any center-based clustering objective over it (such as k-means). Moreover, these solutions can be found exceedingly quickly and are themselves necessarily hierarchical. Thus, given a cluster tree, we show that one can quickly access a plethora of new, equally meaningful hierarchies. Just as in standard hierarchical clustering, one can then choose any desired partition from these new hierarchies. We conclude by verifying the utility of our proposed techniques across datasets, hierarchies, and partitioning schemes. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fast pattern matching approach for intrusion detection systems

Fast pattern matching approach for intrusion detection syste...

引用

1st International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2012

作者： Manjunath, M. Srinivasa, K.G. Muppalla, Anil Kumar Machine Learning and Data Mining Laboratory Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9788132211563

Intrusion detection system (IDS) consists of set of techniques and methods for collection of packets from host system or network and analyzes those packets for anomalous content. IDSs mainly fall into two categories: signature-based IDSs and anomaly detection systems. A rule-based IDS compares the incoming packets against rule set in order to detect intrusion. A common approach followed is to build rule trees or finite automata with rule set and traverse it using a packet as input string. 30-60 % of total signature-based IDS processing time is spent on pattern matching [1]. The existing signature-based IDS cannot meet the speed demands imposed by both high network speeds and increasing number of signatures, and more CPU time is spent on searching for rules that match each packet. In this paper, we are going to present an analysis on IDS that is combined with other methods and techniques to produce greater results and hence contribute to the improvement of IDS. © 2014 Springer India.

关键词： Pattern matching

来源：评论

学校读者我要写书评

暂无评论

Efficient image representation for object recognition via pivots selection

引用

Frontiers of Computer Science 2015年第3期9卷 383-391页

作者： Bojun XIE Yi LIU HuiZHANG Jian YU Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well- known pre-defined patch-level descriptors such as scalein- variant feature transform （SIFT） and histogram of oriented gradient （HOG）, the kernel descriptor （KD） method [1] of- fers a new way to ＂grow-up＂ features from a match-kernel defined over image patch pairs using kernel principal compo- nent analysis （KPCA） and yields impressive results. In this paper, we present efficient kernel descriptor （EKD） and efficient hierarchical kernel descriptor （EHKD）, which are built upon incomplete Cholesky decomposition. EKD au- tomatically selects a small number of pivot features for gener- ating patch-level features to achieve better computational effi- ciency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD.

关键词： efficient kernel descriptor efficient hierarchi-cal kernel descriptor incomplete Cholesky decomposition,patch-level features image-level features

来源：评论

学校读者我要写书评

暂无评论

Pattern Recognition for Brain-Computer Interface on Disabled Subjects using a Wavelet Transformation

Pattern Recognition for Brain-Computer Interface on Disabled...

引用

IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Bassani, Thiago Nievola, Julio Cesar PUCPR Data Mining and Machine Learning Research Group Brazil PUCPR PPGIa (Programa de Pós-Graduação em Informática) Brazil

ISBN: (纸本)9781424417780

The main objective of this work is to present an exploratory approach on electroencephalographic (EEG) signal, analyzing the patterns on the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining on EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the signal underlying information content illustration by representing time-frequency patterns on Wavelet Coherence qualitative analysis. Results suggest that the proposed methodology is capable of identifying regions on time-frequency spectrum during the specified task on BCI. Furthermore, an example of a region is identified, and the patterns were classified using a radial basis function neural network (RBF-NN). This innovative characteristic of the process justify the feasibility of the proposed approach on another data mining applications. It can open new physiologic researches on this field and researches on different non-stationary time series analysis.

关键词： Pattern analysis Classification Signal Processing Brain Computer Interface

来源：评论

学校读者我要写书评

暂无评论

Opinion mining in social media: Modeling, simulating, and visualizing political opinion formation in the web 11

Opinion mining in social media: Modeling, simulating, and vi...

引用

12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, dg.o 2011

作者： Kaschesky, Michael Sobkowicz, Pawel Bouchard, Guillaume Bern University of Applied Sciences E-Government Unit Bern Switzerland Xerox Research Center Europe Data Mining and Machine Learning Grenoble France

ISBN: (纸本)9781450307628

Affordable and ubiquitous online communications (social media) provide the means for flows of ideas and opinions and play an increasing role for the transformation and cohesion of society - yet little is understood about how online opinions emerge, diffuse, and gain momentum. To address this problem, an opinion formation framework based on content analysis of social media and sociophysical system modeling is proposed. Based on prior research and own projects, three building blocks of online opinion tracking and simulation are described: (1) automated topic and opinion detection in real-time, (2) topic and opinion modeling and agent-based simulation, and (3) visualizations of topic and opinion networks. Finally, two application scenarios are presented to illustrate the framework and motivate further research. © 2011 ACM.

关键词： Standardization

来源：评论

学校读者我要写书评

暂无评论

Characterization of bacterial operons consisting of two tubulins and a kinesin-like gene by the novel Two-Step Gene Walking method

引用

NUCLEIC ACIDS RESEARCH 2007年第20期35卷 e135-e135页

作者： Pilhofer, Martin Bauer, Andreas Peter Schrallhammer, Martina Richter, Lothar Ludwig, Wolfgang Schleifer, Karl-Heinz Petroni, Giulio Tech Univ Munich Lehrstuhl Mikrobiol D-85354 Freising Weihenstephan Germany Univ Pisa Dipartimento Biol I-56126 Pisa Italy Tech Univ Munich Machine Learning & Data Mining Bioinformat D-85748 Garching Germany

Tubulins are still considered as typical proteins of Eukaryotes. However, more recently they have been found in the unusual bacteria Prosthecobacter (btubAB). In this study, the genomic organization of the btub-genes and their genomic environment were characterized by using the newly developed Two-Step Gene Walking method. In all investigated Prosthecobacters, btubAB are organized in a typical bacterial operon. Strikingly, all-operons comprise a third gene with similarities to kinesin light chain sequences. The genomic environments of the characterized btub-operons are always different. This supports the hypothesis that this group of genes represents an independent functional unit, which was acquired by Prosthecobacter via horizontal gene transfer. The newly developed Two-Step Gene Walking method is based on randomly primed polymerase chain reaction (PCR). It presents a simple workflow, which comprises only two major stepsa Walking-PCR with a single specific outward pointing primer (step 1) and the direct sequencing of its product using a nested specific primer (step 2). Two-Step Gene Walking proved to be highly efficient and was successfully used to characterize over 20 kb of sequence not only in pure culture but even in complex non-pure culture samples.

关键词： .tubulins operons kinesin light Prosthecobacter btub-genes Walking-PCR

来源：评论

学校读者我要写书评

暂无评论

Toward personalized care management of patients at risk- The diabetes case study 11

Toward personalized care management of patients at risk- The...

引用

17th ACM SIGKDD International Conference on Knowledge Discovery and data mining, KDD 2011

作者： Neuvirth, Hani Ozery-Flato, Michal Laserson, Jonathan Rosen-Zvi, Michal Hu, Jianying Kohn, Martin S. Ebadollahi, Shahram Machine Learning and Data Mining Group IBM Research Mount Carmel Haifa 31905 Israel Healthcare Transformation Group IBM Research Hawthorne NY United States

ISBN: (纸本)9781450308137

Chronic diseases constitute the leading cause of mortality in the western world, have a major impact on the patients'quality of life, and comprise the bulk of healthcare costs. Nowadays, healthcare data management systems integrate large amounts of medical information on patients, including diagnoses, medical procedures, lab test results, and more. Sophisticated analysis methods are needed for utilizing these data to assist in patient management and to enhance treatment quality at reduced costs. In this study, we take a first step towards better disease management of diabetic patients by applying state-of-the art methods to anticipate the patient's future health condition and to identify patients at high risk. Two relevant outcome measures are explored: the need for emergency care services and the probability of the treatment producing a sub-optimal result, as defined by domain experts. By identifying the high-risk patients our prediction system can be used by healthcare providers to prepare both financially and logistically for the patient needs. To demonstrate a potential downstream application for the identified high-risk patients, we explore the association between the physician treating these patients and the treatment outcome, and propose a system that can assist healthcare providers in optimizing the match between a patient and a physician. Our work formulates the problem and examines the performance of several learning models on data from several thousands of patients. We further describe a pilot system built on the results of this analysis. We show that the risk for the two considered outcomes can be evaluated from patients'characteristics and that features of the patient-physician match improve the prediction accuracy for the treatment's success. These results suggest that personalized medicine can be valuable for high risk patients and raise interesting questions for future improvements. Copyright 2011 ACM.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：