The aim of this study is to apply and evaluate the usefulness of the hybrid classifier to predict the presence of serious coronary artery disease based on...

A new method of decision tree construction from temporal data is proposed in the paper. This method uses the so-called temporal cuts for binary partition of data in tree nodes. The novelty of the proposed approach is that the quality of cuts is calcu

In this paper, we present the findings of a study focused on discovering process models and tracking their evolution over time. The research specifically targets a distinct category of these models known as behavioral patterns. Consequently, the challenges and techniques addressed here involve temporal data. To demonstrate the issues and methodologies associated with identifying process patterns and their changes, we use illustrative data from the treatment of respiratory failure in premature infants. The main achievement of the paper is to successfully model behavioral patterns using machine learning models and predict changes in a neonate’s state. That was justified by comparing the classifier’s sensitivity for cases of deterioration, improvement, and stability. Classification quality is best when the pattern remains constant. However, in the proposed model, when the pattern deteriorates, the classification quality decreases only slightly.

A new method of constructing classifiers from huge volume of temporal data is proposed in the paper. The novelty of introduced method finds expression in a multi-stage approach to build hierarchical classifiers that combines process mining, feature e

A method of automatic extracting of temporal patterns from learning data for constructing hierarchical behavioral patterns based classifiers is considered in the paper. The presented approach can be used to complete the knowledge provided by experts or to discover the knowledge automatically if no expert knowledge is accessible. Formal description of temporal patterns is provided and an algorithm for automatic patterns extraction and evaluation is described. A system for packet-based network traffic anomaly detection is used to illustrate the considered ideas.