Petre Lameski

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—Feature selection is important phase in machine learning and in the case of multi-label classification, it can be considerably challenging. In like manner, finding the best subset of good features is involved and difficult when the dataset has significantly large number of features (more than a thousand). In this paper we address the problem of feature(More)
—Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and(More)
Almost all of the machine learning problems require data preprocessing. This stage is especially important for problems where the datasets contain features of mixed types (i.e. nominal and numeric). An often practice in such cases is to transform each nominal features into many dummy (i.e. binary) features. Also many classification algorithms have(More)
In this paper, we describe an approach to the automatic plant identification task of the LifeCLEF 2014 lab. The image descrip-tors for all submitted runs were obtained using the bag-of-visual-words method. For the leaf scans, we use multiscale triangular shape descriptor and for the other plant organs Opponent SIFT extracted around points of interest(More)