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A Comparative Study of Assessment Metrics for Imbalanced Learning (2023)
Conference Proceeding
Farou, Z., Aharrat, M., & Horváth, T. (2023). A Comparative Study of Assessment Metrics for Imbalanced Learning. In New Trends in Database and Information Systems: ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings (119-129). https://doi.org/10.1007/978-3-031-42941-5_11

There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews several metrics for imbalanced learning in binary and multi-class problems. We em... Read More about A Comparative Study of Assessment Metrics for Imbalanced Learning.

Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices (2023)
Conference Proceeding
Antoni, L., Eliaš, P., Horváth, T., Krajči, S., Krídlo, O., & Török, C. (2023). Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices. In Graph-Based Representation and Reasoning: 28th International Conference on Conceptual Structures, ICCS 2023, Berlin, Germany, September 11–13, 2023, Proceedings (19-27). https://doi.org/10.1007/978-3-031-40960-8_2

Formal Concept Analysis identifies hidden patterns in data that can be presented to the user or the data analyst. We propose a method for analyzing the correlation matrices based on Formal concept analysis. In particular, we define a notion of square... Read More about Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices.

Solving Multi-class Imbalance Problems Using Improved Tabular GANs (2022)
Conference Proceeding
Farou, Z., Kopeikina, L., & Horváth, T. (2022). Solving Multi-class Imbalance Problems Using Improved Tabular GANs. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (527-539). https://doi.org/10.1007/978-3-031-21753-1_51

Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, in turn, raises numerous issues for conventional machine learning techniques... Read More about Solving Multi-class Imbalance Problems Using Improved Tabular GANs.

Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring (2022)
Conference Proceeding
Tashu, T. M., & Horváth, T. (2022). Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring. In H. Yin, D. Camacho, & P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022: 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (12-21). https://doi.org/10.1007/978-3-031-21753-1_2

Automatic essay scoring (AES) models based on neural networks (NN) have had a lot of success. However, research has shown that NN-based AES models have robustness issues, such that the output of a model changes easily with small changes in the input.... Read More about Synonym-Based Essay Generation and Augmentation for Robust Automatic Essay Scoring.

Denoising Architecture for Unsupervised Anomaly Detection in Time-Series (2022)
Conference Proceeding
Skaf, W., & Horváth, T. (2022). Denoising Architecture for Unsupervised Anomaly Detection in Time-Series. In S. Chiusano, T. Cerquitelli, R. Wrembel, K. Nørvåg, B. Catania, G. Vargas-Solar, & E. Zumpano (Eds.), New Trends in Database and Information Systems: ADBIS 2022 Short Papers, Doctoral Consortium and Workshops: DOING, K-GALS, MADEISD, MegaData, SWODCH, Turin, Italy, September 5–8, 2022, Proceedings (178-187). https://doi.org/10.1007/978-3-031-15743-1_17

Anomalies in time-series provide insights of critical scenarios across a range of industries, from banking and aerospace to information technology, security, and medicine. However, identifying anomalies in time-series data is particularly challenging... Read More about Denoising Architecture for Unsupervised Anomaly Detection in Time-Series.

Migrating Models: A Decentralized View on Federated Learning (2021)
Conference Proceeding
Kiss, P., & Horváth, T. (2021). Migrating Models: A Decentralized View on Federated Learning. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I (177-191). https://doi.org/10.1007/978-3-030-93736-2_15

Federated learning (FL) researches attempt to alleviate the increasing difficulty of training machine learning models, when the training data is generated in a massively distributed way. The key idea behind these methods is moving the training to loc... Read More about Migrating Models: A Decentralized View on Federated Learning.

Time-Series in Hyper-parameter Initialization of Machine Learning Techniques (2021)
Conference Proceeding
Horváth, T., Mantovani, R. G., & de Carvalho, A. C. P. L. F. (2021). Time-Series in Hyper-parameter Initialization of Machine Learning Techniques. In Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings (246-258). https://doi.org/10.1007/978-3-030-91608-4_25

Initializing the hyper-parameters (HPs) of machine learning (ML) techniques became an important step in the area of automated ML (AutoML). The main premise in HP initialization is that a HP setting that performs well for a certain dataset(s) will als... Read More about Time-Series in Hyper-parameter Initialization of Machine Learning Techniques.

Attention-Based Multi-modal Emotion Recognition from Art (2021)
Conference Proceeding
Tashu, T. M., & Horváth, T. (2021). Attention-Based Multi-modal Emotion Recognition from Art. In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part III (604-612). https://doi.org/10.1007/978-3-030-68796-0_43

Emotions are very important in dealing with human decisions, interactions, and cognitive processes. Art is an imaginative human creation that should be appreciated, thought-provoking, and elicits an emotional response. The automatic recognition of em... Read More about Attention-Based Multi-modal Emotion Recognition from Art.

A Novel Evaluation Metric for Synthetic Data Generation (2020)
Conference Proceeding
Galloni, A., Lendák, I., & Horváth, T. (2020). A Novel Evaluation Metric for Synthetic Data Generation. In Intelligent Data Engineering and Automated Learning – IDEAL 2020: 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II (25-34). https://doi.org/10.1007/978-3-030-62365-4_3

Differentially private algorithmic synthetic data generation (SDG) solutions take input datasets Dp consisting of sensitive, private data and generate synthetic data Ds with similar qualities. The importance of such solutions is increasing both becau... Read More about A Novel Evaluation Metric for Synthetic Data Generation.

Data Generation Using Gene Expression Generator (2020)
Conference Proceeding
Farou, Z., Mouhoub, N., & Horváth, T. (2020). Data Generation Using Gene Expression Generator. In Intelligent Data Engineering and Automated Learning – IDEAL 2020: 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II (54-65). https://doi.org/10.1007/978-3-030-62365-4_6

Generative adversarial networks (GANs) could be used efficiently for image and video generation when labeled training data is available in bulk. In general, building a good machine learning model requires a reasonable amount of labeled training data.... Read More about Data Generation Using Gene Expression Generator.

Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing (2019)
Conference Proceeding
Tashu, T. M., Szabó, D., & Horváth, T. (2019). Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing. In Intelligent Tutoring Systems: 15th International Conference, ITS 2019, Kingston, Jamaica, June 3–7, 2019, Proceedings (186-192). https://doi.org/10.1007/978-3-030-22244-4_23

Automated essay evaluation systems use machine learning models to predict the score for an essay. For such, a training essay set is required which is usually created by human requiring time-consuming effort. Popular choice for scoring is a nearest ne... Read More about Reducing Annotation Effort in Automatic Essay Evaluation Using Locality Sensitive Hashing.

Intelligent On-line Exam Management and Evaluation System (2019)
Conference Proceeding
Tashu, T. M., Esclamado, J. P., & Horvath, T. (2019). Intelligent On-line Exam Management and Evaluation System. In Intelligent Tutoring Systems. ITS 2019 (105-111). https://doi.org/10.1007/978-3-030-22244-4_14

Educational assessment plays a central role in the teaching-learning process as a tool for evaluating students’ knowledge of the concepts associated with the learning objectives. The evaluation and scoring of essay answers is a process, besides being... Read More about Intelligent On-line Exam Management and Evaluation System.

New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings (2018)
Conference Proceeding
(2018). New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings. In A. Benczúr, B. Thalheim, . T. Horváth, . S. Chiusano, T. Cerquitelli, C. Sidló, & P. Z. Revesz (Eds.), . https://doi.org/10.1007/978-3-030-00063-9