The goal of supervised learning is to build a concise model of the distribution of class labels
in terms of predictor features. The resulting classifier is then used to assign class labels to …

Supervised classification is one of the tasks most frequently carried out by so-called
Intelligent Systems. Thus, a large number of techniques have been developed based on …

The paper presents the proposed research methodology, dedicated to the application of
greedy heuristics as a way of gathering information about available features. Discovered …

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 …

State-of-the-art results in typical classification tasks are mostly achieved by unexplainable
machine learning methods, like deep neural networks, for instance. Contrarily, in this paper …

AI solutions are heavily dependant on the quality and accuracy of the input training data,
however the training data may not always fully reflect the most up-to-date policy landscape …

We describe a systematic approach called reframing, defined as the process of preparing a
machine learning model (eg, a classifier) to perform well over a range of operating contexts …

Abstract Knowledge elicitation is known to be a difficult task and thus a major bottleneck in
building a knowledge base. Machine learning has long ago been proposed as a way to …

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

It is crucial for each rule induced via machine learning algorithm is to be associated with a
numerical value (s) which can reflect its properties like accuracy, coverage, likelihood. The …