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Chapter and Conference Paper
PIQARD System for Experimenting and Testing Language Models with Prompting Strategies
Large Language Models (LLMs) have seen a surge in popularity due to their impressive results in natural language processing tasks, but there are still challenges to be addressed. Prompting in the question is a...
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Chapter and Conference Paper
Multi-criteria Approaches to Explaining Black Box Machine Learning Models
The adoption of machine learning algorithms, especially in critical domains often encounters obstacles related to the lack of their interpretability. In this paper we discuss the methods producing local explan...
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Chapter and Conference Paper
Quality Versus Speed in Energy Demand Prediction
Effective heat energy demand prediction is essential in combined heat power systems. The algorithms considered so far do not sufficiently take into account the computational costs and ease of implementation in...
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Chapter
Roman Słowiński and His Research Program: Intelligent Decision Support Systems Between Operations Research and Artificial Intelligence
This chapter is aimed to present the genesis and the development of the scientific research activity of Roman Słowiński considering his contributions in Operations Research, Multiple Criteria Decision Aiding, ...
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Chapter
Rule Confirmation Measures: Properties, Visual Analysis and Applications
According to Bayesian confirmation theory, for a E → H rule, evidence E confirms hypothesis H when E and H are positively probabilistically correlated. Surprisingly, this leads to a plethora of non-equivalent qua...
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Article
Open AccessThe impact of data difficulty factors on classification of imbalanced and concept drifting data streams
Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio...
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Chapter and Conference Paper
multi-imbalance: Open Source Python Toolbox for Multi-class Imbalanced Classification
This paper presents multi-imbalance, an open-source Python library, which equips the constantly growing Python community with appropriate tools to deal with multi-class imbalanced problems. It follows the code co...
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Chapter and Conference Paper
Classification of Multi-class Imbalanced Data: Data Difficulty Factors and Selected Methods for Improving Classifiers
The multiple class imbalanced problem is still less investigated than its binary counterpart. In particular, the sources of its difficulties have not been sufficiently studied so far. Therefore, in this paper ...
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Chapter and Conference Paper
Time Aspect in Making an Actionable Prediction of a Conversation Breakdown
Online harassment is an important problem of modern societies, usually mitigated by the manual work of website moderators, often supported by machine learning tools. The vast majority of previously developed m...
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Chapter and Conference Paper
Prototypical Convolutional Neural Network for a Phrase-Based Explanation of Sentiment Classification
The attention mechanisms are often used to support an interpretation of neural network based classification of texts by highlighting words to which the network paid attention while making a prediction. Followi...
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Article
Open AccessMulti-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data
Roughly Balanced Bagging is one of the most efficient ensembles specialized for class imbalanced data. In this paper, we study its basic properties that may influence its good classification performance. We ex...
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Chapter and Conference Paper
An Algorithm for Selective Preprocessing of Multi-class Imbalanced Data
In this paper we propose a new algorithm called SPIDER3 for selective preprocessing of multi-class imbalanced data sets. While it borrows selected ideas (i.e., combination of relabeling and local resampling) f...
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Chapter
Improving Bagging Ensembles for Class Imbalanced Data by Active Learning
Extensions of under-sampling bagging ensemble classifiers for class imbalanced data are considered. We propose a two phase approach, called Actively Balanced Bagging, which aims to improve recognition of minor...
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Chapter
Local Data Characteristics in Learning Classifiers from Imbalanced Data
Learning classifiers from imbalanced data is still one of challenging tasks in machine learning and data mining. Data difficulty factors referring to internal and local characteristics of class distributions d...
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Article
Open AccessPrequential AUC: properties of the area under the ROC curve for data streams with concept drift
Modern data-driven systems often require classifiers capable of dealing with streaming imbalanced data and concept changes. The assessment of learning algorithms in such scenarios is still a challenge, as exis...
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Chapter and Conference Paper
Tetrahedron: Barycentric Measure Visualizer
Each machine learning task comes equipped with its own set of performance measures. For example, there is a plethora of classification measures that assess predictive performance, a myriad of clustering indice...
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Chapter and Conference Paper
Actively Balanced Bagging for Imbalanced Data
Under-sampling extensions of bagging are currently the most accurate ensembles specialized for class imbalanced data. Nevertheless, since improvements of recognition of the minority class, in this type of ense...
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Chapter and Conference Paper
Evaluating Difficulty of Multi-class Imbalanced Data
Multi-class imbalanced classification is more difficult than its binary counterpart. Besides typical data difficulty factors, one should also consider the complexity of relations among classes. This paper intr...
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Reference Work Entry In depth
Stream Classification
Compared to batch learning from static data, constructing classifiers from data streams implies new requirements for algorithms, such as constraints on memory usage, restricted processing time, and one scan of...
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Chapter and Conference Paper
Discovering Minority Sub-clusters and Local Difficulty Factors from Imbalanced Data
Learning classifiers from imbalanced data is particularly challenging when class imbalance is accompanied by local data difficulty factors, such as outliers, rare cases, class overlap**, or minority class de...
