• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.179-198
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• 2008

It is widely known that usability is a critical quality attribute of IT systems. Many studies have developed various methods for finding out usability problems. Usability professionals have emphasized that usability should be integrated into the development life cycle in order to maximize the usability of systems with minimal cost. To achieve this, it is essential to classify usability problems systematically and connect them into the activities of designing user interfaces and tasks. However, there is a lack of framework or method for these two problems and thus remains a challengeable research issue. As a beginning study, this paper proposes a conceptual framework for addressing the two issues. We firstly summarize usability-related studies so far, including usability factors and evaluation methods. Secondly, we review seven approaches to identifying and classifying usability problems. Based on this review and opinions of usability engineers in real industry as well as the review results, this paper proposes a framework comprising three viewpoints, from which more sound classification scheme of usability problems can be inductively developed.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.123-123
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• 2003

More elaborated methods allowing the usage of binary classifiers for the resolution of multi-class classification problems are briefly presented. This way of using FSVC to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K FSVC solving a one-per-class decomposition of the general problem.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.23-41
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• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.2
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• pp.5-34
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• 1998

The purpose of this study is to develop an idealistic scheme for the classification of Korean music. The ideal classification of Korean music should cover as much knowledge and materials of Korean music as possible. In this study, therefore, Korean music, Korean musicology and music materials were examined first as the backgrounds. Then classification schedules for Korean music including 679 Korean music of KDC were selected, and their expansion aspects and the problems were analyzed. The conditions and the possibility of developing an ideal classification schedule of Korean music were sought through reanalyzing the problems found in these existing classification schedules. As the result of this study a new classification schedule of Korean music was proposed.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.89-96
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• 2007

Various classification methods have been developed to reuse components. These classification methods enable the user to access the needed components quickly and easily. Conventional classification approaches include the following problems: a labor-intensive domain analysis effort to build a classification structure, the representation of the inter-component relationships, difficult to maintain as the domain evolves, and applied to a limited domain. In order to solve these problems, this paper describes a composite cluster analysis approach for component classification. The cluster analysis approach is a combination of a hierarchical cluster analysis method, which generates a stable clustering structure automatically, and a non-hierarchical cluster analysis concept, which classifies new components automatically. The clustering information generated from the proposed approach can support the domain analysis process.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.3
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• pp.109-122
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• 2010

This study is to suggest some ideas for improvements of classification and expansion of the arts in the KDC. In order to this study, analysed changes of terminology, auxiliary tables and notes, and expansion of classification number of the arts from 1st edition to 5th edition of the KDC. The arts of KDC did not changed from 1st to 3rd edition and changed in the 4th edition and 5th edition, and errors and problems of previous edition were not improved, and Classification number and expansion of KDC found out poor rather than different classification schedule because had a lot of Including notes. The result of analysis proposed to improved method to solve the problems.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.793-808
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• 2017

We compare various variable screening methods on multiclass classification problems when the data is ultrahigh-dimensional. Two different approaches were considered: (1) pairwise extension from binary classification via one versus one or one versus rest comparisons and (2) direct classification of multiclass responses. We conducted extensive simulation studies under different conditions: heavy tailed explanatory variables, correlated signal and noise variables, correlated joint distributions but uncorrelated marginals, and unbalanced response variables. We then analyzed real data to examine the performance of the methods. The results showed that model-free methods perform better for multiclass classification problems as well as binary ones.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.3
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• pp.53-72
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• 2013

For the purpose of improving the Korean Decimal Classification (KDC) in the design field, this study investigated the classification systems of design research suggested in previous studies and compared KDC, DDC, LCC, and NDC. The problems identified from the current KDC include lack of subdivisions regarding basic design theories and major design application fields and the absence of notes for explaining the scope of each design field. To solve these problems, this study suggested improvements for design theories, graphic design, industrial design, and environmental design.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.