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Funkcje oceny jakości reguł w algorytmie generowania reguł aproksymacyjnych.

100%

Sikora M. , Proksa P.

tom Vol. 21, nr 3

161-176

W pracy przedstawiono wpływ funkcji oceny jakości reguł, używanej podczas uczenia się reguł, na osiąganą dokładność klasyfikacji oraz na liczbę i złożoność reguł opisujących uczone klasy decyzyjne. Pod tym kątem rozpatrzono trzy funkcje jakości reguł. Podano pewne modyfikacje tych funkcji. Przedstawiono wyniki eksperymentów na trzech zbiorach danych.

In this article one introduced influence of rules quality function used during learning rules on classification accuracy and on number of rules described decision classes. Three rule quolity functions were considered. Some modifications of these functions were introduced. The article includes results of experiments conduced for three different data sets.

An Enhancement of Reinforcement Learning by Scheduling with Learning Effects

26%

Rudek R.

Annals of Computer Science and Information Systems

2023

tom Vol. 35

689--697

This paper present results, which reveal that approaches obtained for scheduling problems with learning effects can be successfully used to improve the quality of machine learning methods. It is illustrated by modelling some aspects of Q-learning agents as scheduling problems with the learning effect, and constructing sequencing and dispatching algorithms, which take into account the existence of learning. Their application to determine the sequence of tasks processed by Q-learning agents can visibly speed up their convergence to an optimal strategy. Furthermore, we show that a dispatch of tasks according to the longest processing time algorithm for parallel computing can be replaced by a more efficient procedure, if agents can learn. The numerical analysis reveals that our approach is efficient, robust and only marginally dependents on a learning model and an accurate approximation of task processing times.

Diagnosing Machine Learning Problems in Federated Learning Systems: A Case Study

26%

Bogacka K. , Danilenka A. , Wasielewska-Michniewska K.

Annals of Computer Science and Information Systems

2023

tom Vol. 35

871--876

The proliferation of digital artifacts with various computing capabilities, along with the emergence of edge computing, offers new possibilities for the development of Machine Learning solutions. These new possibilities have led to the popularity of Federated Learning (FL). While there are many existing works focusing on various aspects of the FL process, the issue of the effective problem diagnosis in FL systems remains largely unexplored. In this work, we have set out to artificially simulate the training process of four selected approaches to FL topology and compare their resulting performance. After noticing concerning disturbances throughout their training process, we have successfully identified their source as the problem of exploding gradients. We have then made modifications to the model structure and analyzed the new results. Finally, we have proposed continuous monitoring of the FL training process through the local computation of a selected metric.