Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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End date: 2009Has files: No

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    Regression Trees from Data Streams with Drift Detection
    (Springer Berlin Heidelberg, 2009)
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    Gama, João
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    Sebastião, Raquel
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    Gjorgjevik, Dejan
    The problem of extracting meaningful patterns from time changing data streams is of increasing importance for the machine learning and data mining communities. We present an algorithm which is able to learn regression trees from fast and unbounded data streams in the presence of concept drifts. To our best knowledge there is no other algorithm for incremental learning regression trees equipped with change detection abilities. The FIRT-DD algorithm has mechanisms for drift detection and model adaptation, which enable to maintain accurate and updated regression models at any time. The drift detection mechanism is based on sequential statistical tests that track the evolution of the local error, at each node of the tree, and inform the learning process for the detected changes. As a response to a local drift, the algorithm is able to adapt the model only locally, avoiding the necessity of a global model adaptation. The adaptation strategy consists of building a new tree whenever a change is suspected in the region and replacing the old ones when the new trees become more accurate. This enables smooth and granular adaptation of the global model. The results from the empirical evaluation performed over several different types of drift show that the algorithm has good capability of consistent detection and proper adaptation to concept drifts.
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    Real-Time Systems - Biologically Inspired Future
    (International Academy Publishing (IAP), 2008-03-01)
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    Bozinovski, Stevo
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    Jovancevski, Gjorgji
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    Asymptotic Improvement of the Binary Gilbert-Varshamov Bound on the Code Rate
    (2009-03-02)
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    We compute the code parameters for binary linear codes obtained by greedy constructing the parity check matrix. Then we show that these codes improve the Gilbert-Varshamov (GV) bound on the code size and rate. This result counter proves the conjecture on the asymptotical exactness of the binary GV bound.
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    Dynamic Evolution and Improvement of Data Warehouse Design
    (IEEE, 2009)
    Sahpaski, Dragan
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    Frequently changing business requirements result not only in changesto the data warehouse (DW) workload but also in changes to the schemastructure. These schema changes introduce new DW versions and so theproblem of optimization of all of its versions becomes a very challengingone.In this paper we present a new approach for dynamically evolving thedesign of the implementation schema spanning across its multiple versions.A new derivation procedure for self improvement of the design andthe performance of the model is applied in several steps. We alsodefine a system for optimization of the design of the multiversionDW schema and its particular instances and include other factors relevantfor optimization of the system i.e. view selection, vertical viewfragmentation and index selection. The self improvement of the multiversionschema design is shown by performing large set of experiments.
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    Improvement of Data Warehouse Optimization Process by Workflow Gridification
    (Springer Berlin Heidelberg, 2008)
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    Cerepnalkoski, Darko
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    Framework for Workflow Gridication of Genetic Algorithms in Java
    (Springer Berlin Heidelberg, 2008)
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    Cerepnalkoski, Darko
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