Journal Information
International Journal of Approximate Reasoning (IJAR)
http://www.journals.elsevier.com/international-journal-of-approximate-reasoning/
Impact Factor:
1.982
Publisher:
Elsevier
ISSN:
0888-613X
Viewed:
8499
Tracked:
12

Call For Papers
The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest.

Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning.

Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.

The journal is affiliated with the North American Fuzzy Information Processing Society (NAFIPS), and collaborates with the Society for Imprecise Probability: Theories and Applications (SIPTA).
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Probability and Statistics: Foundations and History. In honor of Glenn Shafer
Submission Date: 2020-10-21

Glenn Shafer will be 75 in November 2021. This Special Issue of the International Journal of Approximate Reasoning in his honor, and on occasion of this event, will be devoted to the main areas of his research. Glenn’s main research interest has been in the foundations of probability and statistics. While he was still a PhD student he became interested in Arthur Dempster's novel ideas in statistical inference, developing them into what later became known as Dempster-Shafer theory. He continued with deep analyses of causality and graphical models, contributing both to their mathematics and to their philosophy. Later he greatly contributed to developing Philip Dawid's prequential principle, to overcome limitations of measure-theoretic probability. His interest in the foundations has been complemented and reinforced by his interest in the history of probability and statistics. The Special Issue will mainly contain original research articles written by Glenn's colleagues and fellow researchers, describing new developments and results in the areas in which Glenn has been active, including: Theory of belief functions; Graphical models and causal inference; Game-theoretic probability; Conformal prediction; History of probability and statistics.
Last updated by Dou Sun in 2020-05-27
Special Issue on Time Series Clustering and Classification
Submission Date: 2020-12-01

Observation-based (or raw data-based) clustering: it relies on raw data to conduct the cluster analysis, by using suitable metrics based on cross sectional and/or longitudinal characteristics. Model-based clustering: it considers the features of the models fitted to the time series, e.g. ARIMA models, GARCH models, TAR models, splines, distribution models, functional models. Feature-based clustering: it relies on features derived from the observed time series, e.g. autocorrelation, quantile autocovariance, cross correlation, periodogram and its transformations, coherence, wavelets, cepstral. The second part is supervised and commonly referred to as classification, in which case knowledge of prior groupings is available. Classification is a supervised approach to grouping together items of interest and discriminant analysis, neural networks and machine learning methods are amongst the methodological approaches that are used. Time series classification methods include predominantly the use of feature-based, model-based and machine learning techniques. The features are extracted in the time domain, the frequency domain and the wavelet domain. Model-based approaches for time series classification include ARIMA models, Gaussian mixture models and Bayesian approaches, while machine learning approaches include classification trees, nearest neighbour methods and support vector machines. Example of time series classification is the discriminant analysis of electrocardiogram (ECG) signals for three leads based on a three-dimensional formulation of a single dipole of the heart. Contributions on both topics are welcomed from academic researchers in any field, analysts in statistics, data science, computer science, engineering, economics and finance, business and industry, management and marketing, medicine, environment science and hydrology, physics, biology and genetics, and many others. Topics of particular interest include, but are not limited to: · Hierarchical and non-hierarchical clustering of time series · Observation-based clustering of time series · Feature-based clustering of time series · Model-based clustering of time series · Fuzzy clustering for time series · Robust clustering for time series · Mixture clustering models for time series · Self-Organizing Maps for time series · Hidden Markov models for time seriess · Support Vector machines for time series · Classification trees for time series · Discriminant analysis for time series · Bayesian clustering and classification for time series · Neural networks for time series · Nearest neighbours methods for time series · Unsupervised learning for time series · Supervised learining for time series · Clustering and classification of big time series · Clustering and classification of interval time series
Last updated by Dou Sun in 2020-07-30
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