Faculty of Electrical Engineering and Information Technologies

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Author: search.filters.author.Gjoreski, HristijanSubject: search.filters.subject.Machine Learning

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    Item type:Publication,
    Classifying Power Quality Disturbances in Noisy Conditions using Machine Learning
    (The Jozhef Stefan Institute, 2019-10)
    Velichkovska, Bojana
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    Markovska, Marija
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    Gjoreski, Hristijan
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    When ensuring high-quality power supply of the power grid it is of the upmost importance to correctly detect and classify any power quality (PQ) disturbance. Selecting the most relevant features is very important in the process of training a genera machine learning model. Therefore, we analyze the power signals and extract information from them, and then select the most significant features. Additionally, an effective classification model is required. In this study we apply grid search throughout the features sets on one side, and the classification algorithms on the side. This way, we determine the most effective combination of an algorithm and feature set for classification of power quality disturbances.
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    Investigating Presence of Ethnoracial Bias in Clinical Data using Machine Learning
    (2021-09)
    Velichkovska, Bojana
    ;
    Gjoreski, Hristijan
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    ;
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    Celi, Leo Anthony
    An important target for machine learning research is obtaining unbiased results, which require addressing bias that might be present in the data as well as the methodology. This is of utmost importance in medical applications of machine learning, where trained models should be unbiased so as to result in systems that are widely applicable, reliable and fair. Since bias can sometimes be introduced through the data itself, in this paper we investigate the presence of ethnoracial bias in patients’ clinical data. We focus primarily on vital signs and demographic information and classify patient ethnoraces in subsets of two from the three ethnoracial groups (African Americans, Caucasians, and Hispanics). Our results show that ethnorace can be identified in two out of three patients, setting the initial base for further investigation of the complex issue of ehtnoracial bias.