Ikonomovska, Elena

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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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Using data mining technique for coefficient tuning of an adaptive Tabu search
(IEEE, 2007-09-09)
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Gjorgjevikj, Dejan
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Loshkovska, Suzana
This paper describes the Adaptive Tabu Search algorithm (A-TS), an improved tabu search algorithm for combinatorial optimization. A-TS uses a novel approach for evaluation of the moves, incorporated in a new complex evaluation function. A new decision making mechanism triggers the evaluation function providing means for avoiding possible infinite loops. The new evaluation function implements effective diversification strategy that prevents the search from stagnation. It also incorporates two adaptive coefficients that control the influence of the aspiration criteria and the long-term memory, respectively. The adaptive nature of A-TS is based on these two adaptive coefficients. This article also presents a new data mining approach towards improving the performance of A-TS by tuning these coefficients. A-TS performance is applied to the Quadratic Assignment Problem. Published results from other authors are used for comparison. The experimental results show that A-TS performs favorably against other established techniques.
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Using data mining technique for coefficient tuning of an adaptive Tabu search
(IEEE, 2007-09-09)
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Gjorgjevikj, Dejan
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Loshkovska, Suzana
This paper describes the Adaptive Tabu Search algorithm (A-TS), an improved tabu search algorithm for combinatorial optimization. A-TS uses a novel approach for evaluation of the moves, incorporated in a new complex evaluation function. A new decision making mechanism triggers the evaluation function providing means for avoiding possible infinite loops. The new evaluation function implements effective diversification strategy that prevents the search from stagnation. It also incorporates two adaptive coefficients that control the influence of the aspiration criteria and the long-term memory, respectively. The adaptive nature of A-TS is based on these two adaptive coefficients. This article also presents a new data mining approach towards improving the performance of A-TS by tuning these coefficients. A-TS performance is applied to the Quadratic Assignment Problem. Published results from other authors are used for comparison. The experimental results show that A-TS performs favorably against other established techniques.
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A survey of stream data mining
(2007)
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Gjorgjevikj, Dejan
At present a growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. In this paper, we present the theoretical foundations of data stream analysis and identify potential directions of future research. Mining data stream techniques are being critically reviewed.
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The Adaptive Tabu Search and Its Application to the Quadratic Assignment Problem
(2006)
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This article presents a new algorithm for combinatorial optimization based on the basic Tabu Search scheme named Adaptive Tabu Search (A-TS). The A-TS introduces a new, complex function for evaluation of moves. The new evaluation function incorporates both the aspiration criteria and the longterm memory. A-TS also introduces a new decision making mechanism, providing means for avoiding possible infinite loops. The performance of A-TS was measured by applying it to the Quadratic Assignment Problem. The experimental results are compared to published results from other authors. The data shows that A-TS performs favorably against other established techniques.

Ikonomovska, Elena

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