Jakub Klikowski

Jakub Klikowski
Wroclaw University of Science and Technology | WUT · Department of Systems and Computer Networks

PhD

About

10
Publications
711
Reads
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30
Citations

Publications

Publications (10)
Article
Full-text available
One of the most critical data analysis tasks is the streaming data classification, where we may also observe the concept drift phenomenon, i.e., changing the decision model’s probabilistic characteristics. From a practical point of view, we may face this type of banking, medicine, or cybersecurity task to enumerate only a few. A vital characteristi...
Chapter
One of the current challenges for the supervised classification methods is to obtain acceptable values of the performance measures for an imbalanced dataset. There is a significant disproportion in the number of objects from different class labels in datasets with a high imbalanced ratio. This article analyzes the clustering and weighted scoring al...
Chapter
Imbalanced datasets are still a big method challenge in data mining and machine learning. Various machine learning methods and their combinations are considered to improve the quality of the classification of imbalanced datasets. This paper presents the approach with the clustering and weighted scoring function based on geometric space are used. In...
Article
The imbalanced data classification remains a vital problem. The key is to find such methods that classify both the minority and majority class correctly. The paper presents the classifier ensemble for classifying binary, non-stationary and imbalanced data streams where the Hellinger Distance is used to prune the ensemble. The paper includes an expe...
Preprint
Full-text available
The imbalanced data classification remains a vital problem. The key is to find such methods that classify both the minority and majority class correctly. The paper presents the classifier ensemble for classifying binary, non-stationary and imbalanced data streams where the Hellinger Distance is used to prune the ensemble. The paper includes an expe...
Chapter
The classification of imbalanced data streams is gaining more and more interest. However, apart from the problem that one of the class is not well represented, there are problems typical for data stream classification, such as limited resources, lack of access to the true labels and the possibility of occurrence of the concept drift. Possibility of...
Chapter
Imbalanced data streams have gained significant popularity among the researchers in recent years. This area of research is not only still greatly underdeveloped, but there are also numerous inherent difficulties that need to be addressed when creating algorithms that could be utilized in such dynamic environment and achieve satisfactory results whe...
Chapter
The classification of data streams is a frequently considered problem. The data coming in over time has a tendency to change its characteristics over time and usually we also encounter some difficulties in data distributions as inequality of the number of learning examples from considered classes. The combination of these two phenomena is an additi...
Chapter
Imbalanced data classification is still a focus of intense research, due to its ever-growing presence in the real-life decision tasks. In this article, we focus on a classifier ensemble for imbalanced data classification. The ensemble is formed on the basis of the individual classifiers trained on supervise-selected feature subsets. There are sever...

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