Mitreski, Kosta

Preferred name

Mitreski, Kosta

Official Name

Mitreski, Kosta

Main Affiliation

kosta.mitreski@finki.ukim.mk

Now showing 1 - 10 of 73

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Evaluation of Yager Families of Aggregation Operators in Discovering the Diatoms Indicating Properties
(IEEE, 2018-10)
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Mirceva, Georgina
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Fuzzy aggregation operators perform operations between two fuzzy sets that satisfy certain axioms. They play an important role in fuzzy data mining process. As an integral part of many algorithms, the aggregation operators influence on the outcome of model and thus on the experimental evaluation of the models. Both pattern tree (PT) and the weighted pattern tree (WPT) algorithms use the aggregation operators to increase the accuracy of the model by making different operations between the descriptive and target attributes. Selecting the right operator is very important, especially considering generalized families of aggregation operators. Therefore, this paper aims to investigate the influence of the generalized Yager families of aggregation operators and their influence on both (PT and WPT) algorithms accuracy. This is done by modifying the λ parameter. This parameter is not the only parameter that influences the model performance, other factors are also in play, like the shape and the number of the membership functions (MFs), as well as the similarity metric. Our experimental evaluation will evaluate the descriptive and predictive performance of the models as well as the statistical significance of the results. The evaluation results show that the best descriptive and predictive models with both PT and WPT algorithms are obtained when λ is set to 1. For future work, we plan to investigate the influence of this family of aggregation operators with different similarity metrics, as well as other datasets.
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Ski Slopes Digitalization and Rating Analysis of Ski Resorts in Mavrovo and Popova Shapka
(IEEE, 2019)
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Winter months are about snow, and there is no greater benefit for people that live in polluted cities to spend some time on high mountains in fresh air and sun. Therefore, the economical developing of location that offers such winter recreational activities is very important. In this direction, this paper aims to use Geographic Information Systems (GIS) to improve the understanding of the relationship between the ski resorts and a user rating of satisfactory opinion through data analysis and digitalization. GIS is a powerful digitalization and analytical tool and most importantly, the users can access the platform from anywhere and anytime. As a case study, we have considered two major ski resorts in our country, as well as rating data for customer satisfaction scores. Furthermore, as a showcase, we have digitalized one ski resorts slope. The results of this research will provide a framework for future analysis and the development of ski tourism. In the future, we plan to advance this further improve the data analysis with other data and advance the decision-making process.
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Evaluation of diatoms biodiversity models by applying different discretization on the class attribute
(IEEE, 2020-09-28)
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One of the main goals of knowledge discovery from environmental data is through data analysis to find the relationship between the living organisms, represented with the diversity of the diatoms community members, and the characteristics of the environment. This is very important information for both ecologists and decision makers. Therefore, in this paper we apply various machine learning algorithms for revealing this relationship by using different number of discretization levels for the target attribute. The target attribute represents the biodiversity index of the community and it is calculated based on the abundances of the diatoms. For building models, different types of machine learning algorithms are considered including decision trees, rule induction algorithms, neural networks and Naïve Bayes. The obtained models are also examined regarding resistance to over-fitting, as well as statistical significance.
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Modelling the Impact of the Hotel Facilities on Online Hotel Review Score for City of Skopje
(IEEE, 2018-10)
Stojchevska, Marija
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Having a reliable and objective opinion regarding their hotel facilities is very important for hotel managers. In today's internet-connected world where each year millions of travelers book, stay in, and review hotel accommodations, it is very easy to obtain desired information with the right tools. This study utilizes online data generated from thousands of guests' hotel reviews to provide comparative and benchmarking insights about the customer satisfaction through hotel review scores. Skopje, an emerging tourist destination in the Republic of Macedonia, was selected for this study. The data for this study consisted of over a thousand user-generated reviews for 80 hotels that were posted on Booking.com and Tripadviser.com. Twenty hotel facilities influencing the hotel review score were identified, such as Sauna, Spa, Fitness Center, Soundproof, Free Parking and others. Relating the review score with hotel facilities was done through visual display and graphic analysis using the ArcGIS software. This enabled us to compare the hotel review score between 3, 4 and 5-star hotels and the hotel facilities. Depending on the hotel manager's needs, the users can browse hotels through ArcGIS map hotels according to popularity, number of stars, or distance to the city center. Many of the generated graph results revealed interesting patterns that can deliver powerful feedback relationships for researchers as well as hotel managers. In the future we plan to expand our analysis on other cities to provide comprehensives data statistics for the tourist sector in Republic of Macedonia.
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Ski Slopes Digitalization and Rating Analysis of Ski Resorts in Mavrovo and Popova Shapka
(IEEE, 2019-10)
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Bodybuilding Gyms and Sports Goods Centers in Skopje
(IEEE, 2019)
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The purpose of this work is to bring bodybuilding closer to our everyday lives. Most of the time, we might feel unmotivated to go to a gym, giving ourselves unreasonable excuses, such as, there is no gym near our homes, or that its open hours do not fit our busy schedule. This project makes a contribution in three ways: 1) by collecting data for gyms in the capital city of our country, we try to undermine the excuses, 2) once the excuses are eliminated, by visual representation on the map, and by interpolation of the ratings given to each gym, we try to recommend a gym to everyone in Skopje, 3) we analyze the spatial correlation of gym ratings with the variety of sport equipment offered in the sport stores near-by.
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Experimental Evaluation of Different Membership Functions on Weighted Pattern Trees for Diatom Modelling
(IEEE, 2018-07)
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Mirceva, Georgina
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Weighted Pattern Trees (WPT) algorithm is an extension of the pattern tree algorithm, which builds models with different weights and these weights are used for predicting the particular output attribute and they show how much a particular tree model is confident to predict such class. The WPT uses the similarity information between the fuzzy term leaf and the root of the tree model to weight the model. Each fuzzy term is acquired from the input dataset using different types of membership functions (MFs). The shape and mathematical formulation of the MFs plays an important role in the WPT algorithm induction, and thus on the model performance. In this direction, the paper aims to experimentally investigate the influence of three smoothed MFs on real measured ecological dataset using three different type of experiments. The first experiments evaluate the influence of the number of MFs per attribute, the second experiments examine the type of the MFs, and the third experiments investigate the influence of different WPT variants on both descriptive and predictive classification accuracy. The results for the statistical significance with the two-step procedure, showed that models with depth 10 with Sigmoidal +1 MF and high number of MFs per attribute are excellent for building models with high descriptive power. On the other side, the models with low number of MFs with Bell MF and model depth constrained to five have high predictive power. These results encourage us to further investigate the influence of different similarity metrics and fuzzy aggregation operators on the performance of WPT models.
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Implication of Hamacher T-norm on Two Fuzzy-Rough Rule Induction Algorithms
(IEEE, 2022-05-23)
Naumoski, Anreja
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Mirceva, Georgina
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From the rule induction algorithms we can obtain models in If-Then form that are very easy to be interpreted by humans. To further improve this class of algorithms, in this paper we focus on QuickRules and Vaguely Quantified Rough fuzzy-rough rule induction algorithms, by introducing the novel Hamacher T-norm. It is important to know that T-norms as well as the fuzzy tolerance relationship metrics, implicators and vague quantifiers play an important role in model accuracy because they are used to calculate the lower and upper approximations. For this purpose, in our models’ evaluation, we use five fuzzy tolerance relationship metrics to evaluate the performance of the models that are obtained with the new Hamacher T-norm. The AUC ROC metric was used to evaluate the performance, and later was used to evaluate the statistical significance. The results revealed that fuzzy tolerance relationship metrics have greater influence than the k-parameter from the Hamacher T-norm on models’ performance, and this was also compared to the vaguely quantified algorithm that uses vague quantifiers. For future work, we plan to conduct further investigation of the influence of another T-norms and fuzzy tolerance relationship metrics on this type of algorithms.
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Influence of the Yu T-norm on Vaguely Quantified Rough Set Measure Algorithm Accuracy
(IEEE, 2022-11-16)
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Mirceva, Georgina
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Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms
(Inderscience Publishers (IEL), 2022)
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Mirceva, Georgina
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The fuzzy-rough rule induction algorithms use fuzzy-rough set concepts such as t-norms, implicators and fuzzy tolerance relationship metrics to calculate the upper and lower approximations. In this direction, the paper examines the influence of the novel Webber t-norm on the model performance obtained with the QuickRules and VQRules algorithms over 19 datasets from different research disciplines. The AUC-ROC metric is used to assess model performance as well as the statistical significance compared to the control model with the highest rank. The obtained results revealed that the k-parameter of the Webber t-norm decreases the model descriptive performance as his value increases, but for the predictive performance of the model there was not any influence by this parameter. In both cases, we were able to identify specific algorithm settings, mostly specific metrics for fuzzy tolerance relations that produce models with high accuracy.

Mitreski, Kosta

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