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: 2009Language: search.filters.language.English

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    A Multi-class SVM Classifier Utilizing Binary Decision Tree
    (2009-05)
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    In this paper a novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass problems is presented. The hierarchy of binary decision subtasks using SVMs is designed with a clustering algorithm. For consistency between the clustering model and SVM, the clustering model utilizes distance measures at the kernel space, rather than at the input space. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The SVMBDT architecture was designed to provide superior multi-class classification performance. Its performance was measured on samples from MNIST, Pendigit, Optdigit and Statlog databases of handwritten digits and letters. The results of the experiments indicate that while maintaining comparable or offering better accuracy with other SVM based approaches, ensembles of trees (Bagging and Random Forest) and neural network, the training phase of SVM-BDT is faster. During recognition phase, due to its logarithmic complexity, SVM-BDT is much faster than the widely used multi-class SVM methods like “one-against-one” and “one-against-all”, for multiclass problems. Furthermore, the experiments showed that the proposed method becomes more favourable as the number of classes in the recognition problem increases.
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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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    Improving Multilevel Approach for Optimizing Collective Communications in Computational Grids
    (Springer Berlin Heidelberg, 2005)
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    Collective operations represent a tool for easy implementation of parallel algorithms in the message-passing parallel programming languages. Efficient implementation of these operations significantly improves the performance of the parallel algorithms, especially in the Grid systems. We introduce an improvement of multilevel algorithm that enables improvement of the performance of collective communication operations. An implementation of the algorithm is used for analyzing its characteristics and for comparing its performance it with the multilevel algorithm.