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1.
FairBoost: Boosting supervised learning for learning on multiple sensitive features
Ivona Colakovic, Sašo Karakatič, 2023, original scientific article

Abstract: The vast majority of machine learning research focuses on improving the correctness of the outcomes (i.e., accuracy, error-rate, and other metrics). However, the negative impact of machine learning outcomes can be substantial if the consequences marginalize certain groups of data, especially if certain groups of people are the ones being discriminated against. Thus, recent papers try to tackle the unfair treatment of certain groups of data (humans), but mostly focus on only one sensitive feature with binary values. In this paper, we propose an ensemble boosting FairBoost that takes into consideration fairness as well as accuracy to mitigate unfairness in classification tasks during the model training process. This method tries to close the gap between proposed approaches and real-world applications, where there is often more than one sensitive feature that contains multiple categories. The proposed approach checks the bias and corrects it through the iteration of building the boosted ensemble. The proposed FairBoost is tested within the experimental setting and compared to similar existing algorithms. The results on different datasets and settings show no significant changes in the overall quality of classification, while the fairness of the outcomes is vastly improved.
Keywords: fairness, boosting, machine learning, supervised learning
Published in DKUM: 11.06.2024; Views: 49; Downloads: 2
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Machine learning driven extended matrix norm method for the solution of large-scale zero-sum matrix games
Burhaneddin İzgi, Murat Özkaya, Nazım Kemal Üre, Matjaž Perc, 2023, original scientific article

Abstract: In this paper, we develop a novel machine learning-driven framework for solving large-scale zero-sum matrix games by exploiting patterns discovered from the offline extended matrix norm method. Modern game theoretic tools such as the extended matrix norm method allow rapid estimation of the game values for small-scale zero-sum games by computing norms of the payoff matrix. However, as the number of strategies in the game increases, obtaining an accurate value estimation through the extended matrix norm method becomes more difficult. In this work, we propose a novel neural network architecture for large-scale zero-sum matrix games, which takes the estimations of the extended matrix norm method and payoff matrix as inputs, and provides a rapid estimation of the game value as the output. The proposed architecture is trained over various random zero-sum games of different dimensions. Results show that the developed framework can obtain accurate value predictions, with a less than 10% absolute relative error, for games with up to 50 strategies. Also of note, after the network is trained, solution predictions can be obtained in real-time, which makes the proposed method particularly useful for real-world applications.
Keywords: machine learning, EMN method, large-scale games, zero-sum games, approximated solutions
Published in DKUM: 31.05.2024; Views: 49; Downloads: 0
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4.
Machine learning partners in criminal networks
Diego D. Lopes, Bruno R. da Cunha, Alvaro F. Martins, Sebastián Gonçalves, Ervin K. Lenzi, Quentin S. Hanley, Matjaž Perc, Haroldo V. Ribeiro, 2022, original scientific article

Abstract: Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among diferent types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with signifcant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.
Keywords: machine learning, crime, network, social physics
Published in DKUM: 28.05.2024; Views: 569; Downloads: 0

5.
Adaptive boosting method for mitigating ethnicity and age group unfairness
Ivona Colakovic, Sašo Karakatič, 2024, original scientific article

Abstract: Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced biases. Therefore, this paper deals with a common unfairness problem, unequal quality of service, that appears in classification when age and ethnicity groups are used. To tackle this issue, we propose an adaptive boosting algorithm that aims to mitigate the existing unfairness in data. The proposed method is based on the AdaBoost algorithm but incorporates fairness in the calculation of the instance’s weight with the goal of making the prediction as good as possible for all ages and ethnicities. The results show that the proposed method increases the fairness of age and ethnicity groups while maintaining good overall quality compared to traditional classification algorithms. The proposed method achieves the best accuracy in almost every sensitive feature group. Based on the extensive analysis of the results, we found that when it comes to ethnicity, interestingly, White people are likely to be incorrectly classified as not being heroin users, whereas other groups are likely to be incorrectly classified as heroin users.
Keywords: fairness, boosting, machine learning, classification
Published in DKUM: 24.05.2024; Views: 203; Downloads: 7
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Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case study
Miha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, original scientific article

Abstract: Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
Keywords: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Published in DKUM: 25.03.2024; Views: 209; Downloads: 10
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8.
Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
Bojan Žlahtič, Jernej Završnik, Helena Blažun Vošner, Peter Kokol, David Šuran, Tadej Završnik, 2023, original scientific article

Abstract: Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Keywords: XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Published in DKUM: 14.03.2024; Views: 219; Downloads: 19
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Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review
Lucija Gosak, Adrijana Svenšek, Mateja Lorber, Gregor Štiglic, 2023, review article

Abstract: Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one.
Keywords: artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review
Published in DKUM: 27.11.2023; Views: 330; Downloads: 15
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