Gjorgji Madjarov

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Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. We selected(More)
Gait is a persons manner of walking. It is a biometric that can be used for identifying humans. Gait is an unobtrusive metric that can be obtained from distance, and this is its main strength compared to other biometrics. In this paper we construct and evaluate feature sets with the purpose of finding out the role of different types of features and body(More)
A common approach to solving multi-label learning problems is to use problem transformation methods and dichotomizing classifiers as in the pair-wise decomposition strategy. One of the problems with this strategy is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning(More)
In this paper, we present a novel deep learning architecture for sentiment analysis in Twitter messages. Our system finki, employs both convolutional and gated recurrent neural networks to obtain a more diverse tweet representation. The network is trained on top of GloVe word embeddings pre-trained on the Common Crawl dataset. Both neural networks are used(More)
In this paper we depict an implemented system for medical image retrieval. Our system performs retrieval based on both textual and visual content, separately and combined, using advanced encoding and quantization techniques. The text-based retrieval subsystem uses textual data acquired from an image’s corresponding article to generate a suitable(More)
When an RNA primary sequence is folded back on itself, forming complementary base-pairs, a form called RNA secondary structure is created. The first solution for the RNA secondary structure prediction problem was the Nussinov dynamic programming algorithm developed in 1978 which is still an irreplaceable base that all other approaches rely on. In this work,(More)
Multi-label classification (MLC) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLC are the large-scale problem and the high dimensionality of the label space, which have a strong(More)
Multi-label learning (MLL) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. An issues that severely limits the applicability of many current machine learning approaches to MLL are the large-scale problem, which have a strong impact on the computational complexity of(More)