Delev, Tomche

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Efficient Two Stage Voting Architecture for Pairwise Multi-label Classification
(Springer Berlin Heidelberg, 2010)
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A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of labels. To tackle this problem we propose a two stage voting architecture (TSVA) for efficient pair-wise multiclass voting to the multi-label setting, which is closely related to the calibrated label ranking method. Four different real-world datasets (enron, yeast, scene and emotions) were used to evaluate the performance of the TSVA. The performance of this architecture was compared with the calibrated label ranking method with majority voting strategy and the quick weighted voting algorithm (QWeighted) for pair-wise multi-label classification. The results from the experiments suggest that the TSVA significantly outperforms the concurrent algorithms in term of testing speed while keeping comparable or offering better prediction performance.
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A study on implementation and usage of web based programming assessment system: Code
(2014)
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Gjorgjevikj, Dejan
Implementing a web-based system for automatic assessment is a big step in the introductionary programming courses. In this paper we study and report the data generated by the usage of the system Code developed at the Faculty of Computer Science and Engineering. The system supports compilation and execution of programming problems in exercises and exams and it is used in many courses that involve programming assignments. The analyzed data shows the differences in working in laboratory settings, compared to practical exams. We also present the results from plagiarism detection, and report significant cases of plagiarism in introductionary courses. At the end we present the results from initial qualitative evaluation of the system by surveying 48 students.
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Estimation of Functional Brain Connectivity
(Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia, 2012)
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Madjarov, Gjorgji
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Gjorgjevikj, Dejan
This article presents an overview about the basis of estimators of connectivity, such as Directed Transfer Function (DTF) and Partial Directed Coherence (PDC), their differences and their applicability in the estimation and analysis of functional brain connectivity.
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Static analysis of source code written by novice programmers
(IEEE, 2017)
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In this paper we are reporting the finding on the use of a static analysis of C source code written by students learning to program. Two different tools for static code analysis were used to analyze the solutions submitted by the students on the partial exams and exams from the introductory course in programming in a three year period. We have collected, analyzed and compared most common errors reported by both tools. We further investigate if the available checks provided by these tools, often used in professional software development practices to find bugs and improve the code quality, can also help novice programmers in tracking down and resolving their problems in the code or have any other value in the process of learning programming.
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Ensembles of binary svm decision trees
(2010)
Madjarov, Gjorgji
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Gjorgjevikj, Dejan
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Ensemble methods are able to improve the predictive performance of many base classifiers. In this paper, we consider two ensemble learning techniques, bagging and random forests, and apply them to Binary SVM Decision Tree (SVM-BDT). Binary SVM Decision Tree is a tree based architecture that utilizes support vector machines for solving multiclass problems. It takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. In this paper we empirically investigate the performance of ensembles of SVM-BDTs. Our most important conclusions are: (1) ensembles of SVM-BDTs yield noticeable better predictive performance than the base classifier (SVM-BDT), and (2) the random forests ensemble technique is more suitable than bagging for SVMBDT.
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Place-Tags, discovering and promoting places through mobile phones and collaborative filtering
(IEEE, 2010-06-21)
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Gjorgjevikj, Dejan
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Madjarov, Gjorgji
This paper presents the design and implementation of a mobile application along with a web server for geo-tagging favorite and interesting places and sharing them with the community. The design and architecture shows some key aspects and issues concerning this kind of system. The mobile application is implemented in J2ME and tested on GPS enabled Nokia phones and the web server is implemented on cloud infrastructure implementation, the Google App Engine. The system was evaluated with real devices and a proof of concept was made that applications such as Place-Tags has its place in the mobile world.

Delev, Tomche

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