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All Outputs (36)

Cluster-based oversampling with area extraction from representative points for class imbalance learning (2024)
Journal Article
Farou, Z., Wang, Y., & Horváth, T. (2024). Cluster-based oversampling with area extraction from representative points for class imbalance learning. Intelligent Systems with Applications, 22, Article 200357. https://doi.org/10.1016/j.iswa.2024.200357

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small dis... Read More about Cluster-based oversampling with area extraction from representative points for class imbalance learning.

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)
Journal Article
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Junior, S., Vanschoren, J., & de Carvalho, A. C. P. L. F. (2024). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38, 1364–1416. https://doi.org/10.1007/s10618-024-01002-5

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex i... Read More about Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.

Object Detection Using Sim2Real Domain Randomization for Robotic Applications (2022)
Journal Article
Horváth, D., Erdős, G., Istenes, Z., Horváth, T., & Földi, S. (2023). Object Detection Using Sim2Real Domain Randomization for Robotic Applications. IEEE Transactions on Robotics, 39(2), 1225-1243. https://doi.org/10.1109/tro.2022.3207619

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different ind... Read More about Object Detection Using Sim2Real Domain Randomization for Robotic Applications.

Dynamic noise filtering for multi-class classification of beehive audio data (2022)
Journal Article
Várkonyi, D. T., Seixas Junior, J. L., & Horváth, T. (2023). Dynamic noise filtering for multi-class classification of beehive audio data. Expert Systems with Applications, 213(Part A), Article 118850. https://doi.org/10.1016/j.eswa.2022.118850

Honeybees are the most specialized insect pollinators and are critical not only for honey production but, also, for keeping the environmental balance by pollinating the flowers of a wide variety of crops.

Recording and analyzing bee sounds became... Read More about Dynamic noise filtering for multi-class classification of beehive audio data.

Tracing the Local Breeds in an Outdoor System – A Hungarian Example with Mangalica Pig Breed (2022)
Book Chapter
Alexy, M., & Horváth, T. Tracing the Local Breeds in an Outdoor System – A Hungarian Example with Mangalica Pig Breed. In Tracing the Domestic Pig. IntechOpen. https://doi.org/10.5772/intechopen.101615

Pig farming is largely characterized by closed, large-scale housing technology. These systems are driven by resource efficiency. In intensive technologies, humans control almost completely. However, there are pig farming systems where humans have jus... Read More about Tracing the Local Breeds in an Outdoor System – A Hungarian Example with Mangalica Pig Breed.

Linear Concept Approximation for Multilingual Document Recommendation (2021)
Book Chapter
Salamon, V. T., Tashu, T. M., & Horváth, T. (2021). Linear Concept Approximation for Multilingual Document Recommendation. . Springer. https://doi.org/10.1007/978-3-030-91608-4_15

In this paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to... Read More about Linear Concept Approximation for Multilingual Document Recommendation.

Multimodal Emotion Recognition from Art Using Sequential Co-Attention (2021)
Journal Article
Tashu, T. M., Hajiyeva, S., & Horvath, T. (2021). Multimodal Emotion Recognition from Art Using Sequential Co-Attention. Journal of Imaging, 7(8), Article 157. https://doi.org/10.3390/jimaging7080157

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the... Read More about Multimodal Emotion Recognition from Art Using Sequential Co-Attention.

Swarm intelligence techniques in recommender systems - A review of recent research (2019)
Journal Article
Peška, L., Tashu, T. M., & Horváth, T. (2019). Swarm intelligence techniques in recommender systems - A review of recent research. Swarm and Evolutionary Computation, 48, 201-219. https://doi.org/10.1016/j.swevo.2019.04.003

One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS.... Read More about Swarm intelligence techniques in recommender systems - A review of recent research.

Evolutionary computing in recommender systems: a review of recent research (2016)
Journal Article
Horváth, T., & de Carvalho, A. C. P. L. F. (2017). Evolutionary computing in recommender systems: a review of recent research. Natural Computing, 16(3), 441-462. https://doi.org/10.1007/s11047-016-9540-y

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. O... Read More about Evolutionary computing in recommender systems: a review of recent research.

Buried pipe localization using an iterative geometric clustering on GPR data (2013)
Journal Article
Janning, R., Busche, A., Horváth, T., & Schmidt-Thieme, L. (2014). Buried pipe localization using an iterative geometric clustering on GPR data. Artificial Intelligence Review, 42(3), 403-425. https://doi.org/10.1007/s10462-013-9410-2

Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects cause hyperbola shaped reflections in the radargram images achieved by GPR. Ori... Read More about Buried pipe localization using an iterative geometric clustering on GPR data.

Factorization Techniques for Predicting Student Performance (2012)
Book Chapter
Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., Nanopoulos, A., & Schmidt-Thieme, L. (2012). Factorization Techniques for Predicting Student Performance. In O. C. Santos, & J. G. Boticario (Eds.), Educational Recommender Systems and Technologies: Practices and Challenges (129-153). IGI Global. https://doi.org/10.4018/978-1-61350-489-5.ch006

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to students. This chapter introduces state-of-the-art recommender system techni... Read More about Factorization Techniques for Predicting Student Performance.

GRAMOFON: General model-selection framework based on networks (2011)
Journal Article
Buza, K., Nanopoulos, A., Horváth, T., & Schmidt-Thieme, L. (2012). GRAMOFON: General model-selection framework based on networks. Neurocomputing, 75(1), 163-170. https://doi.org/10.1016/j.neucom.2011.02.026

Ensembles constitute one of the most prominent class of hybrid prediction models. One basically assumes that different models compensate each other's errors if one combines them in an appropriate way. Often, a large number of various prediction model... Read More about GRAMOFON: General model-selection framework based on networks.

User Preference Web Search -- Experiments with a System Connecting Web and User (2009)
Journal Article
Gurský, P., Horvath, T., Jirásek, J., Krajči, S., Novotny, R., Pribolová, J., Vaneková, V., & Vojtáš, P. (2009). User Preference Web Search -- Experiments with a System Connecting Web and User. Computing and Informatics, 28(4), 1001-1033

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware support... Read More about User Preference Web Search -- Experiments with a System Connecting Web and User.

A Model of User Preference Learning for Content-Based Recommender Systems (2009)
Journal Article
Horvath, T. (2009). A Model of User Preference Learning for Content-Based Recommender Systems. Computing and Informatics, 28(4), 1001-1029

This paper focuses to a formal model of user preference learning for
content-based recommender systems. First, some fundamental and special requirements to user preference learning are identified and proposed. Three learning tasks are introduced as... Read More about A Model of User Preference Learning for Content-Based Recommender Systems.

Integration of two fuzzy data mining methods (2004)
Journal Article
Horvath, T., & Krajči, S. (2004). Integration of two fuzzy data mining methods. Neural Network World, 14(5), 391-402

The cluster analysis and the formal concept analysis are both used to identify significiant groups of similar objects. Rice & Siff's algorithm for the clustering joins these two methods in the case where the values of an object-attribute model are 1... Read More about Integration of two fuzzy data mining methods.

Data Generation Using Gene Expression Generator
Presentation / Conference Contribution
Farou, Z., Mouhoub, N., & Horváth, T. (2020, November). Data Generation Using Gene Expression Generator. Presented at IDEAL 2020: 21st International Conference on Intelligent Data Engineering and Automated Learning, Guimarães, Portugal

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.

Denoising Architecture for Unsupervised Anomaly Detection in Time-Series
Presentation / Conference Contribution
Skaf, W., & Horváth, T. (2022, September). Denoising Architecture for Unsupervised Anomaly Detection in Time-Series. Presented at ADBIS 2022: 26th European Conference on Advances in Databases and Information Systems, Turin, Italy

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.

Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices
Presentation / Conference Contribution
Antoni, L., Eliaš, P., Horváth, T., Krajči, S., Krídlo, O., & Török, C. (2023, September). Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices. Presented at International Conference on Conceptual Structures (ICCS) 2023, Berlin

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.

Migrating Models: A Decentralized View on Federated Learning
Presentation / Conference Contribution
Kiss, P., & Horváth, T. (2021, September). Migrating Models: A Decentralized View on Federated Learning. Presented at ECML PKDD 2021, Online

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.