Piotr Synak

Rough set algorithms in classification problem

We we present some algorithms, based on rough set theory, that can be used for the problem of new cases classification. Most of the algorithms were implemented and included in Rosetta system [43]. We present several methods for computation of decision rules based on reducts. We discuss the problem of real value attribute discretization for increasing the performance of algorithms and quality of decision rules. Finally we deal with a problem of resolving conflicts between decision rules classifying a new case to different categories (classes). Keywords: knowledge discovery, rough sets, classification algorithms, reducts, decision rules, real value attribute discretization

Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables

We apply rough set methods and boolean reasoning for knowledge discovery from decision tables. It is not always possible to extract general laws from experimental data by computing first all reducts [12] of a decision table and next decision rules on the basis of these reducts. We investigate a problem how information about the reduct set changes in a random sampling process of a given decision table could be used to generate these laws. The reducts stable in the process of decision table sampling are called dynamic reducts. Dynamic reducts define the set of attributes called the dynamic core. This is the set of attributes included in all dynamic reducts. The set of decision rules can be computed from the dynamic core or from the best dynamic reducts. We report the results of experiments with different data sets, e.g. market data, medical data, textures and handwritten digits. The results are showing that dynamic reducts can help to extract laws from decision tables.

Brighthouse: an analytic data warehouse for ad-hoc queries

Brighthouse is a column-oriented data warehouse with an automatically tuned, ultra small overhead metadata layer called Knowledge Grid, that is used as an alternative to classical indexes. The advantages of column-oriented data storage, as well as data compression have already been well-documented, especially in the context of analytic, decision support querying. This paper demonstrates additional benefits resulting from Knowledge Grid for compressed, column-oriented databases. In particular, we explain how it assists in query optimization and execution, by minimizing the need of data reads and data decompression.

Approximation spaces and information granulation

In this paper, we discuss approximation spaces in a granular computing framework. Such approximation spaces generalise the approaches to concept approximation existing in rough set theory. Approximation spaces are constructed as higher level information granules and are obtained as the result of complex modelling. We present illustrative examples of modelling approximation spaces that include approximation spaces for function approximation, inducing concept approximation, and some other information granule approximations. In modelling of such approximation spaces we use an important assumption that not only objects but also more complex information granules involved in approximations are perceived using only partial information about them.

Multi-Label Classification of Emotions in Music

This paper addresses the problem of multi-label classification of emotions in musical recordings. The testing data set contains 875 samples (30 seconds each). The samples were manually labelled into 13 classes, without limits regarding the number of labels for each sample. The experiments and test results are presented.

Discovery of Data Patterns with Applications to Decomposition and Classification Problems

Data mining community is searching for efficient methods of extracting patterns from data [20],[22],[39],[46],[45]. We study problems of extracting several kinds of patterns from data. The simplest ones are called templates. We consider also more sophisticated relational patterns extracted automatically from data.

Rough sets and information granulation

In this paper, the study of the evolution of approximation space theory and its applications is considered in the context of rough sets introduced by Zdzislaw Pawlak and information granulation as well as computing with words formulated by Lotfi Zadeh. Central to this evolution is the rough-mereological approach to approximation of information granules. This approach is built on the inclusion relation to be a part to a degree, which generalises the rough set and fuzzy set approaches. An illustration of information granulation of relational structures is given. The contribution of this paper is a comprehensive view of the notion of information granule approximation, approximation spaces in the context of rough sets and the role of such spaces in the calculi of information granules.

Extracting emotions from music data

Music is not only a set of sounds, it evokes emotions, subjectively perceived by listeners. The growing amount of audio data available on CDs and in the Internet wakes up a need for content-based searching through these files. The user may be interested in finding pieces in a specific mood. The goal of this paper is to elaborate tools for such a search. A method for the appropriate objective description (parameterization) of audio files is proposed, and experiments on a set of music pieces are described. The results are summarized in concluding chapter.

Application of Temporal Descriptors to Musical Instrument Sound Recognition

An automatic content extraction from multimedia files is recently being extensively explored. However, an automatic content description of musical sounds has not been broadly investigated and still needs an intensive research. In this paper, we investigate how to optimize sound representation in terms of musical instrument recognition purposes. We propose to trace trends in the evolution of values of MPEG-7 descriptors in time, as well as their combinations. Described process is a typical example of KDD application, consisting of data preparation, feature extraction and decision model construction. Discussion of efficiency of applied classifiers illustrates capabilities of possible progress in the optimization of sound representation. We believe that further research in this area would provide background for an automatic multimedia content description.

KDD-Based Approach to Musical Instrument Sound Recognition

Automatic content extraction from multimedia files is a hot topic nowadays. Moving Picture Experts Group develops MPEG-7 standard, which aims to define a unified interface for multimedia content description, including audio data. Audio description in MPEG-7 comprises features that can be useful for any content-based search of sound files. In this paper, we investigate how to optimize sound representation in terms of musical instrument recognition purposes. We propose to trace trends in evolution of values of MPEG-7 descriptors in time, as well as their combinations. Described process is a typical example of KDD application, consisting of data preparation, feature extraction and decision model construction. Discussion of efficiency of applied classifiers illustrates capabilities of further progress in optimization of sound representation. We believe that further research in this area would provide background for automatic multimedia content description.