In this paper, we provide a brief summary of elementary research in rule learning. The two
main research directions are descriptive rule learning, with the goal of discovering …

Abstract Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-
making processes to build user trust and promote responsible AI. Hence, a key scientific …

Data stream mining is the process of extracting knowledge structures from continuous, rapid
data records. Many decision tasks can be formulated as stream mining problems and …

The paper presents research on unsupervised and supervised discretisation of input data
used in execution of stylometric tasks of authorship attribution. Basing on numeric …

Pierwsza część monografii poświęcona jest pokryciowym algorytmom indukcji reguł i
obiektywnym miarom oceny jakości reguł decyzyjnych. Przedstawiono dwa algorytmy, które …

Rule conflicts can arise in machine learning systems that utilise unordered rule sets. A rule
conflict is when two or more rules cover the same example but differ in their majority classes …

This chapter starts with a general introduction to protein folding. We then present a
probabilistic method of dealing with multi-class classification, in particular multi-class protein …

In the current connected world-Websites, Mobile Apps, IoT Devices collect a large volume of
users' personally identifiable activity data. These collected data is used for varied purposes …

Rule learning comes in many forms, here we investigate a modified version of Separate and
Conquer (SAC) learning to see if it improves the predictive performance of the induced …

In this paper, we present a probabilistic method of dealing with multi-class classification
using Stochastic Logic Programs (SLPs), a Probabilistic Inductive Logic Programming (PILP) …