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A review on evolutionary algorithms in Bayesian network learning and inference tasks

INFORMATION SCIENCES 2013年 233卷 109-125页

作者： Larranaga, Pedro Karshenas, Hossein Bielza, Concha Santana, Roberto Univ Politecn Madrid Computat Intelligence Grp Fac Informat E-28660 Madrid Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastian 20080 Spain

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms. Evolutionary algorithms, as successful problem solvers, are promising candidates for this purpose. This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning. (c) 2013 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical model Bayesian network Evolutionary computation Inference Learning from data

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Provenance-Enabled Explainable AI

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Proceedings of the ACM on Management of Data 2024年第6期2卷 1-27页

作者： Jiachi Zhang Wenchao Zhou Benjamin E. Ujcich Alibaba Cloud Hangzhou China Georgetown University Washington DC USA

Machine learning (ML) algorithms have advanced significantly in recent years, progressively evolving into artificial intelligence (AI) agents capable of solving complex, human-like intellectual challenges. Despite the advancements, the interpretability of these sophisticated models lags behind, with many ML architectures remaining "black boxes" that are too intricate and expansive for human interpretation. Recognizing this issue, there has been a revived interest in the field of explainable AI (XAI) aimed at explaining these opaque ML models. However, XAI tools often suffer from being tightly coupled with the underlying ML models and are inefficient due to redundant computations. We introduce provenance-enabled explainable AI (PXAI). PXAI decouples XAI computation from ML models through a provenance graph that tracks the creation and transformation of all data within the model. PXAI improves XAI computational efficiency by excluding irrelevant and insignificant variables and computation in the provenance graph. Through various case studies, we demonstrate how PXAI enhances computational efficiency when interpreting complex ML models, confirming its potential as a valuable tool in the field of XAI.

关键词： data provenance explainable ai k-means clustering multi-layer perceptron probabilistic graphical model

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Discovering Clinical Information models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR

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JOURNAL OF MEDICAL INTERNET RESEARCH 2019年第5期21卷 e13504.-e13504.页

作者： Yang, Lin Huang, Xiaoshuo Li, Jiao Chinese Acad Med Sci Med Lib Inst Med Informat 3 Yabao Rd Beijing 100020 Peoples R China Peking Union Med Coll 3 Yabao Rd Beijing 100020 Peoples R China

Background: Clinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important. Objective: This study aimed to develop a retrieval method to identify CIMs online to represent EHR data. Methods: We proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics. Results: We downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering "diagnostic reports," "problem list," "patients background," "clinical decision," etc, as well as models that other retrieval methods could not find, such as "problems and diagnoses." Conclusions: The graphical retriev

关键词： openEHR clinical information model health information interoperability information retrieval probabilistic graphical model

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Toward an idiomatic framework for cognitive robotics

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PATTERNS 2022年第7期3卷 100533页

作者： Damgaard, Malte Rormose Pedersen, Rasmus Bak, Thomas Aalborg Univ Dept Elect Syst Automat & Control DK-9220 Aalborg Denmark

Inspired by the "cognitive hourglass'' model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by encouraging cooperation and re-use of existing results. This is done by proposing a framework dividing development of cognitive architectures into a series of layers that can be considered partly in isolation, some of which directly relate to other research fields. Finally, we introduce and review some topics essential for the proposed framework. We also outline a set of applications.

关键词： robotics cognitive robotics cognitive architecture framework probabilistic programming probabilistic programming language probabilistic inference variational inference message-passing algorithm stochastic variational inference probabilistic graphical model

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A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records

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ACCIDENT ANALYSIS AND PREVENTION 2022年 176卷 106790页

作者： Joo, Yang-Jun Kho, Seung-Young Kim, Dong-Kyu Park, Ho-Chul Seoul Natl Univ Dept Civil & Environm Engn Seoul 08826 South Korea Myongji Univ Dept Transportat Engn Yongin 17058 South Korea

In recent years, individual drivers' crash risk assessments have received much attention for identifying high-risk drivers. To this end, we propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e., traffic violations, license, and crash records). In developing this method, we used 7.75 million vi-olations and crashes of 5.5 million individual drivers in Seoul, South Korea, from June 2013 to June 2017 (four years). The stochastic process of the Bayesian network (BN), whose structure is optimized by tabu-search, suc-cessfully evaluates individual drivers' crash and violation probability. In addition, the cluster analysis classifies drivers into five distinctive groups according to their estimated violation and crash probabilities. As a result, this study found that the estimated average crash rate within a cluster converges with the actual crash rate by the proposed framework without privacy issues. We also confirm that violation records and expected crash proba-bility are strongly correlated, and there is a direct relationship between a driver's previous violations and crash record and the future at-fault crash. The proposed assessment method is valuable in developing proactive driver education programs and safety countermeasures, including adjusting the penalty system and developing user -based insurance by recognizing dangerous drivers and identifying their properties.

关键词： Crash risk assessment Bayesian network probabilistic graphical model Traffic crash Traffic violation

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A Dual-Field Sensing Scheme for a Guidance System for the Blind

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SENSORS 2016年第5期16卷 667-667页

作者： Lin, Qing Han, Youngjoon Soongsil Univ Sports IT Convergence Dept 511 Sangdo Dong Seoul 156743 South Korea Soongsil Univ Dept Smart Syst Software 511 Sangdo Dong Seoul 156743 South Korea

An electronic guidance system is very helpful in improving blind people's perceptions in a local environment. In our previous work "Lin, Q.;Han, Y. A Context-Aware-Based Audio Guidance System for Blind People Using a Multimodal Profile model. Sensors 2014, 14, 18670-18700", a context-aware guidance system using a combination of a laser scanner and a camera was proposed. By using a near-field graphical model, the proposed system could interpret a near-field scene in very high resolution. In this paper, our work is extended by adding a far-field graphical model. The integration of the near-field and the far-field models constitutes a dual-field sensing scheme. In the near-field range, reliable inference of the ground and object status is obtained by fusing range data and image data using the near-field graphical model. In the far-field range, which only the camera can cover, the far-field graphical model is proposed to interpret far-field image data based on appearance and spatial prototypes built using the near-field interpreted data. The dual-field sensing scheme provides a solution for the guidance systems to optimise their scene interpretation capability using simple sensor configurations. Experiments under various local conditions were conducted to show the efficiency of the proposed scheme in improving blind people's perceptions in urban environments.

关键词： electronic travel aids guidance systems for blind people sensor fusion scene interpretation probabilistic graphical model context awareness

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CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

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GENOME MEDICINE 2021年第1期13卷 45-45页

作者： Shafighi, Shadi Darvish Kielbasa, Szymon M. Sepulveda-Yanez, Julieta Monajemi, Ramin Cats, Davy Mei, Hailiang Menafra, Roberta Kloet, Susan Veelken, Hendrik van Bergen, Cornelis A. M. Szczurek, Ewa Univ Warsaw Fac Math Informat & Mech Stefana Banacha 2 PL-02097 Warsaw Poland Leiden Univ Dept Biomed Data Sci Med Ctr Einthovenweg 20 NL-2333 ZC Leiden Netherlands Leiden Univ Dept Hematol Med Ctr Albinusdreef 2 NL-2333 ZA Leiden Netherlands Leiden Univ Leiden Genome Technol Ctr Med Ctr Einthovenweg 20 NL-2333 ZC Leiden Netherlands

Background: Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. Methods: Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. Results: We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. Conclusions: The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://***/LUMC/CACTUS).

关键词： Single-cell sequencing Follicular lymphoma B cell receptor Clonal evolution Somatic mutations probabilistic graphical model

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Sublinear Computational Time modeling by Momentum-Space Renormalization Group Theory in Statistical Machine Learning Procedures

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REVIEW OF SOCIONETWORK STRATEGIES 2019年第2期13卷 281-306页

作者： Tanaka, Kazuyuki Ohzeki, Masayuki Yasuda, Muneki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan Tokyo Inst Technol Inst Inovat Recearch Yokohama Kanagawa 2268503 Japan Yamagata Univ Grad Sch Sci & Engn Yonezawa Yamagata 9928510 Japan

We review sublinear computational time modeling using momentum-space renormalization group approaches in the statistical machine learning algorithms. The modeling scheme has been proposed and the basic frameworks have been briefly explained in a short note (Tanaka et al. in J. Phys. Soc. Jpn, 87(8), Article ID: 085001, 2018). We present their detailed formulations and some numerical experimental results of sublinear computational time modeling based on the momentum-space renormalization scheme.

关键词： Statistical machine learning Bayesian modeling probabilistic graphical model Renormalization group Statistical mechanical informatics

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Intersection two-vehicle crash scenario specification for automated vehicle safety evaluation using sequence analysis and Bayesian networks

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ACCIDENT ANALYSIS AND PREVENTION 2022年 176卷 106814页

作者： Song, Yu Chitturi, Madhav V. Noyce, David A. Univ Connecticut Connecticut Transportat Inst 270 Middle Turnpike Storrs CT 06269 USA Univ Wisconsin Dept Civil & Environm Engn Traff Operat & Safety Lab 1415 Engn Dr Madison WI 53706 USA

This paper introduces a test scenario specification procedure using crash sequence analysis and Bayesian network modeling. Intersection two-vehicle crash data was obtained from the 2016-2018 National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS) database. Vehicles involved in the crashes are specifically renumbered based on their initial positions and trajectories. Crash sequences are encoded to include detailed pre-crash events and concise collision events. Based on sequence patterns, the crashes are characterized as 55 types. A Bayesian network model is developed to depict the interrelationships among crash sequence types, crash outcomes, human factors, and environmental conditions. Scenarios are specified by querying the Bayesian network's conditional probability table. Distributions of operational design domain (ODD) attributes (e.g., driver behavior, weather, lighting condition, intersection geometry, traffic control device) are specified based on conditions of sequence types. Also, distribution of sequence types is specified on specific crash outcomes or combinations of ODD attributes.

关键词： Motor vehicle crash Test scenario Sequence of events Operational design domain probabilistic graphical model

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SPIN reveals genome-wide landscape of nuclear compartmentalization

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GENOME BIOLOGY 2021年第1期22卷 1页

作者： Wang, Yuchuan Zhang, Yang Zhang, Ruochi van Schaik, Tom Zhang, Liguo Sasaki, Takayo Peric-Hupkes, Daniel Chen, Yu Gilbert, David M. van Steensel, Bas Belmont, Andrew S. Ma, Jian Carnegie Mellon Univ Sch Comp Sci Computat Biol Dept Pittsburgh PA 15213 USA Netherlands Canc Inst Oncode Inst Plesmanlaan 121 NL-1066 CX Amsterdam Netherlands Netherlands Canc Inst Div Gene Regulat Plesmanlaan 121 NL-1066 CX Amsterdam Netherlands Univ Illinois Dept Cell & Dev Biol Urbana IL 61801 USA Whitehead Inst Biomed Res 9 Cambridge Ctr Cambridge MA 02142 USA Florida State Univ Dept Biol Sci Tallahassee FL 32304 USA Univ Calif Berkeley Dept Mol & Cell Biol 229 Stanley Hall Berkeley CA 94720 USA

We report SPIN, an integrative computational method to reveal genome-wide intranuclear chromosome positioning and nuclear compartmentalization relative to multiple nuclear structures, which are pivotal for modulating genome function. As a proof-of-principle, we use SPIN to integrate nuclear compartment mapping (TSA-seq and DamID) and chromatin interaction data (Hi-C) from K562 cells to identify 10 spatial compartmentalization states genome-wide relative to nuclear speckles, lamina, and putative associations with nucleoli. These SPIN states show novel patterns of genome spatial organization and their relation to other 3D genome features and genome function (transcription and replication timing). SPIN provides critical insights into nuclear spatial and functional compartmentalization.

关键词： Nuclear compartmentalization 3D genome organization Nuclear bodies probabilistic graphical model

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