Reinforcement learning techniques achieved human-level performance in several tasks the last decade. However, recent years, need for interpretability emerged: we want to be able understand how a system works and reasons behind its decisions. Not only assess safety of produced systems, also it extract knowledge about unknown problems. While some that optimize decision trees reinforcement do exis...

The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answer...

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signa...

9 Planned Communities (PCs) present a unique opportunity for deployment of intelligent control of demand-side 10 distributed energy resources (DER) and storage, which may be organized in Microgrids (MGs). MGs require 11 balancing for maintaining safe and resilient operation. This paper discusses the implications of using MG concepts 12 for planning and control of energy systems within PCs. A no...

In this paper we evaluate the use of the machine learning algorithms Support Vector Machines, K-Nearest Neighbors, CART decision trees and Naive Bayes to identify non spontaneous saccades in clinical electrooculography tests. Our approach tries to solve problems like the use of manually established thresholds present in classical methods like identification by velocity threshold (I-VT) or ident...

Abstract This paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one alternatives to top-down inducers. It searches tree structure and tests simultaneously thus gives improvements in prediction size resulting classifiers many situations. However, it population-based iterative that can be too computationally demanding apply big data m...