Rough sets analysis of diagnostic capacity of vibroacoustic symptoms

AbstractThe paper refers to the problem of diagnostic classification of mechanical objects using vibroacoustic symptoms. A new approach based on the rough sets theory is applied to evaluate the symptoms from the point of view of their diagnostic capacity, i.e., the quality of estimation of a technical state of a mechanical object. The approach enables reduction of the set of symptoms to a minimal subset ensuring a satisfactory estimation. The minimal subset is then used to create a classifier of a technical state. Particular attention is paid to a comparison of different methods of calculation of symptom limit values which divide domains of symptoms into intervals corresponding to classes of technical states. The analysed set of data concerns the technical state of rolling bearings installed in a laboratory stand. They are described by a set of symptoms which result from measurements of noise and vibration of bearing housings. The bearings are in good or bad technical states. The paper presents particular steps of the rough sets methodology and gives, as a final result, a classifier of a technical state of bearings based on a minimal subset of symptoms with the greatest diagnostic capacity

Extension of the fuzzy dominance-based rough set approach using ordered weighted average operators

In the article we rst review some known results on fuzzy versions of the dominance-based rough set approach (DRSA) where we expand the theory considering additional properties. Also, we apply Ordinal Weighted Average (OWA) operators in fuzzy DRSA. OWA operators have shown a lot of potential in handling outliers and noisy data in decision tables when it is combined with the indiscernibility-based rough set approach (IRSA).We examine theoretical properties of the proposed combination with fuzzy DRSA

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies