Inducing Jury’s Preferences in Terms of Acoustic Features of Violin Sounds

Jelonek, Jacek; Lukasik, Ewa; Naganowski, Aleksander; Słowiński, Roman

doi:10.1007/978-3-540-24844-6_73

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…A dominance-based rough set approach, an extension of rough sets to preference-ordered information systems, was used in [74] to generate preference models for violin quality grading. A set of violins were submitted to a violinmaker s competition and evaluated by a jury according to several assessment criteria.…”

Section: Music and Acousticsmentioning

confidence: 99%

Rough sets, their extensions and applications

Shen

Jensen

2007

Int J Automat Comput

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Q. Shen and R. Jensen, 'Rough sets, their extensions and applications,' International Journal of Automation and Computing (IJAC), vol. 4, no. 3, pp. 217-218, 2007.Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Despite its recency, the theory and its extensions have been widely applied to many problems, including decision analysis, data-mining, intelligent control and pattern recognition. This paper presents an outline of the basic concepts of rough sets and their major extensions, covering variable precision, tolerance and fuzzy rough sets. It also shows the diversity of successful applications these theories have entailed, ranging from financial and business, through biological and medicine, to physical, art, and meteorological.Peer reviewe

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Section: Music and Acousticsmentioning

confidence: 99%

Rough sets, their extensions and applications

Shen

Jensen

2007

Int J Automat Comput

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“…In some practical problems, there are some attributes with preferenceordered values such as the attribute "score" taking values of high, medium and low. The data with such preferenceorder relations can be described by the ordered information systems (OIS) which are used in many problems such as multi-criteria decision making, credit and risk evaluation, and feasibility study and prediction (Blanco-Fernández et al 2008;Greco et al 1998;Jelonek et al 2004;Chen et al 2011). Greco et al firstly developed the framework of dominance relation-based rough set approach (DRSA), in which dominance relations substitute equivalence relations to describe the preference orders of attribute values (Greco et al 1999(Greco et al , 2001(Greco et al , 2002.…”

Section: Introductionmentioning

confidence: 99%

Update of approximations in ordered information systems under variations of attribute and object set

Hua

2022

Adv. in Comp. Int.

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Many collected data from real world applications often evolve when new attributes or objects are inserted or old ones are removed. The set approximations of ordered information systems (OIS) need to be updated from time to time for further data reduction, analysis, or decision-making. Incremental approaches are feasible and efficient techniques for updating approaches when any variation occurs. In this paper, considering OIS for multi-criteria classification problems, we discuss the principles of incrementally updating approximations in dominance relation based method in four different types of dynamic environments which combine the changes of both attribute set and object set. In each dynamic environment, the corresponding updating principles and algorithm are given with detail proofs. The experimental results and analysis on UCI data sets show that the proposed incremental approach outperforms the non-incremental method and the integration of current incremental algorithms in the implementation efficiency.

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“…Ko and Fujita proposed the notion of evidential probability to express the dominance between attributes, and modified DRSA into an evidential model [19]. Besides the theoretical research, many practical applications based on DRSA were also conducted, such as instruments detection [17], airline market management [28][29][30], business value evaluation [38], and auction mechanisms design [41]. Recently DRSA was further applied to auditing risk judgement [15], candidate supplier selection [15], water quality analysis [18], and vehicle management [1,15].…”

Section: Introductionmentioning

confidence: 99%

Efficient updating rough approximations with multi-dimensional variation of ordered data

Wang

Luo

et al. 2016

Information Sciences

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Ordered data widely exists in practical problems. Dominance-based Rough Set Approach (DRSA) is an effective mathematical tool to obtain approximations of concepts and discovery knowledge from ordered data. In this paper, we focus on the dynamic DRSA for the multi-dimensional variation of an ordered information system, and propose a novel incremental simplified algorithm which can efficiently update approximations of DRSA when objects and attributes increase simultaneously. Most of existing algorithms can efficiently deal with the single-dimensional variation of an information system. However, multi-dimensional variations often occur in real dynamic data. That is, the object set, the attribute set or attribute values of an information system often vary simultaneously. Although we can directly use the definitions of approximations, or integrate some single-dimensional incremental algorithms to cope with multi-dimensional variations, this always results in complex algorithm architectures and a large amount of inefficient computation. In our works, by simplifying traditional definitions of DRSA and employing the incremental learning strategy, we develop an algorithm to neglect unnecessary parameters and avoid much redundant computation. Then, we present two different storage schemes for our proposed algorithm to solve the problem of memory consumption. Finally, a series of experiments are conducted to evaluate its efficiency. Experimental results clearly show that for the two-dimensional variation of objects and attributes in an ordered information system, our proposed algorithm is much faster not only than the non-incremental algorithm based on traditional definitions, but also than the integration of two single-dimensional incremental algorithms.

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Inducing Jury’s Preferences in Terms of Acoustic Features of Violin Sounds

Cited by 7 publications

References 4 publications

Rough sets, their extensions and applications

Rough sets, their extensions and applications

Update of approximations in ordered information systems under variations of attribute and object set

Efficient updating rough approximations with multi-dimensional variation of ordered data

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