International Journal of Information Technology and Decision Making (INT J INF TECH DECIS )

Publisher: World Scientific Publishing

Description

  • Impact factor
    1.31
    Show impact factor history
     
    Impact factor
  • 5-year impact
    1.38
  • Cited half-life
    3.60
  • Immediacy index
    0.13
  • Eigenfactor
    0.00
  • Article influence
    0.18
  • Website
    International Journal of Information Technology and Decision Making website
  • Other titles
    International journal of information technology & decision making (Online)
  • ISSN
    0219-6220
  • OCLC
    53924823
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

World Scientific Publishing

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • On personal website or institutional repository
    • Publisher's version/PDF cannot be used
    • Set statement to accompany preprint and postprint - see policy
  • Classification
    ​ green

Publications in this journal

  • [show abstract] [hide abstract]
    ABSTRACT: Surveillance operators normally analyze vast amounts of sensor data in order to find conflict situations, and threatening or unusual activities while allowing the continuous flow of goods and people. Semi-automatic support may reduce the time needed for the detection of such situations, generating early warnings that can prevent accidents or provide time to prepare countermeasures. In order to provide adequate cognitive support for operators and guide the design of more efficient surveillance systems, this paper investigates the human analytical reasoning process of detecting anomalous behavior through a case study, the surveillance of sea areas. The analysis of data gathered during interviews and participant observations at three maritime control centers and the inspection of video recordings of real incidents lead to a characterization of operators' analytical processes. We suggest how to support these processes using data mining and visualization, and we derive recommendations for designers and developers of future maritime control systems.
    International Journal of Information Technology and Decision Making 01/2014;
  • International Journal of Information Technology and Decision Making 11/2013; 12(6):1333-1360.
  • [show abstract] [hide abstract]
    ABSTRACT: In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.
    International Journal of Information Technology and Decision Making 11/2013; 12(6).
  • [show abstract] [hide abstract]
    ABSTRACT: We introduce a new method for stock keeping unit (SKU)-store level sales prediction in the presence of promotions to support order quantity and promotion planning decisions for retail managers. The method leverages the marketing literature to generate features, and data mining techniques to train a model that provides accurate sales predictions for existing and new SKUs, as well as consistent, actionable insights into category, store and promotion dynamics. The proposed \Driver Moderator" method uses basic SKU-store information and historical sales and promotion data to generate many features. It simultaneously selects few relevant features and estimates their parameters by using an L1-norm regularized epsilon insensitive regression that is formulated to pool information across SKUs and stores. Evaluations on two grocery store databases from Turkey and the USA show that out-of-sample predictions for existing and new SKUs are as good as, or more accurate than benchmark methods. Using the method's predictions for inventory decisions doubles the inventory turn ratio versus using individual regressions by lowering lost sales and inventory levels at the same time.
    International Journal of Information Technology and Decision Making 11/2013; 12(6):1-26.
  • [show abstract] [hide abstract]
    ABSTRACT: The problem of knowledge-based multiattribute classi.cation with nonorderable classes is considered within the Verbal Decision Analysis (VDA) paradigm. Two VDA-based methods for such problem solving – NORClass and STEPCLASS – are outlined. The STEPCLASS method is initially designed for the problem with nonordered values of attributes. The main idea of NORClass is based on the assumption that a domain expert is able to order the values of any attribute according to their typicality for each class differently and independently of the values of other attributes. We propose to integrate into the STEPCLASS method the main ideas from NORClass. It is shown that such integration allows increasing the effciency of the STEPCLASS method in cases, where the above assumption is true, and to overcome some drawbacks of NORClass.
    International Journal of Information Technology and Decision Making 09/2013; 12(5):905-925.
  • [show abstract] [hide abstract]
    ABSTRACT: The electronic mail (email) is nowadays an essential communication service being widely used by most Internet users. One of the main problems affecting this service is the proliferation of unsolicited messages (usually denoted by spam) which, despite the efforts made by the research community, still remains as an inherent problem affecting this Internet service. In this perspective, this work proposes and explores the concept of a novel symbiotic feature selection approach allowing the exchange of relevant features among distinct collaborating users, in order to improve the behavior of anti-spam filters. For such purpose, several Evolutionary Algorithms (EA) are explored as optimization engines able to enhance feature selection strategies within the anti-spam area. The proposed mechanisms are tested using a realistic incremental retraining evaluation procedure and resorting to a novel corpus based on the well-known Enron datasets mixed with recent spam data. The obtained results show that the proposed symbiotic approach is competitive also having the advantage of preserving end-users privacy.
    International Journal of Information Technology and Decision Making 07/2013; 12(04):863-884.
  • International Journal of Information Technology and Decision Making 07/2013; Vol 12(3).
  • International Journal of Information Technology and Decision Making 01/2013; 12(6):1175–1199.
  • [show abstract] [hide abstract]
    ABSTRACT: This paper deals with inverse DEA from both theoretical and applied viewpoints. First, some methodological approaches and theoretical results are outlined; and then an application of DEA with real-world data (for assessing educational departments in a university) is addressed. Afterwards, possible extensions and applications of the existing approaches in the presence of fuzzy data are developed. The final theoretical part of the paper contains a main theorem which provides a sufficient condition for efficiency maintaining in the presence of fuzzy data. To do this, we have used some notions/results from multi-objective decision-making theory.
    International Journal of Information Technology and Decision Making 01/2013;
  • International Journal of Information Technology and Decision Making 01/2013; 12(1):27-44.
  • International Journal of Information Technology and Decision Making 01/2013; In Press.

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