Search Word(s) in Title, Keywords, Authors, and Abstract: Rough Set

Rough Set-Based Approach for Automatic Emotion Classification of Music
Babu Kaji Baniya and Joonwhoan Lee
Page: 400~416, Vol. 13, No.2, 2017
10.3745/JIPS.04.0032
Keywords: Attributes, Covariance, Discretize, Rough Set, Rules
Show / Hide Abstract
Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the 4th order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.


Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction
Wei Xu and Zhi Xiao
Page: 109~128, Vol. 12, No.1, 2016
10.3745/JIPS.04.0016
Keywords: Business Failure Prediction, Combined Forecasting Method, Qualitative Analysis, Quantitative Analysis, Receiver Operating Characteristic Curve, Soft Set Theory
Show / Hide Abstract
This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.


The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing
Witold Pedrycz
Page: 397~412, Vol. 7, No.3, 2011
10.3745/JIPS.2011.7.3.397
Keywords: Information Granularity, Principle of Justifiable Granularity, Knowledge Management, Optimal Granularity Allocation
Show / Hide Abstract
Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.