Zobrazeno 1 - 10
of 240
pro vyhledávání: '"Stefanowski Jerzy"'
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 34, Iss 1, Pp 119-133 (2024)
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining more desired predictions. They can be generated by a variety of methods that optimize various, sometimes conflicting, quality measures an
Externí odkaz:
https://doaj.org/article/32b63df138cb47beae6919d10433237b
Autor:
Przybyl, Bartosz, Stefanowski, Jerzy
Learning classifiers from imbalanced and concept drifting data streams is still a challenge. Most of the current proposals focus on taking into account changes in the global imbalance ratio only and ignore the local difficulty factors, such as the mi
Externí odkaz:
http://arxiv.org/abs/2410.03519
Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static scenarios, real-world applications often involve data or model changes, p
Externí odkaz:
http://arxiv.org/abs/2408.04842
Autor:
Nalepa Grzegorz J., Stefanowski Jerzy
Publikováno v:
Foundations of Computing and Decision Sciences, Vol 45, Iss 3, Pp 159-177 (2020)
In last years Artificial Intelligence presented a tremendous progress by offering a variety of novel methods, tools and their spectacular applications. Besides showing scientific breakthroughs it attracted interest both of the general public and indu
Externí odkaz:
https://doaj.org/article/6dad711d4a444e579bea9391a1432e67
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 29, Iss 4, Pp 769-781 (2019)
The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting infor
Externí odkaz:
https://doaj.org/article/63ee10335bee4cf8a04908075d7cccf4
Autor:
Karolczak, Jacek, Stefanowski, Jerzy
The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection
Externí odkaz:
http://arxiv.org/abs/2405.21036
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
Growing regulatory and societal pressures demand increased transparency in AI, particularly in understanding the decisions made by complex machine learning models. Counterfactual Explanations (CFs) have emerged as a promising technique within Explain
Externí odkaz:
http://arxiv.org/abs/2405.17642
We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization
Externí odkaz:
http://arxiv.org/abs/2405.17640
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 27, Iss 4, Pp 669-679 (2017)
This paper shows how big data analysis opens a range of research and technological problems and calls for new approaches. We start with defining the essential properties of big data and discussing the main types of data involved. We then survey the d
Externí odkaz:
https://doaj.org/article/2ac4b4fb3b9741d39fc74284c7ae9f75
Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user. Although several methods for providing such explanations have recently been proposed, we argue that an important aspect of e
Externí odkaz:
http://arxiv.org/abs/2312.11356