• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• 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.

• Journal of Power Electronics
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• v.2 no.2
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• pp.104-111
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• 2002

This paper presents a new hybrid control scheme using Active-Clamped Class-E(ACCE) inverter for the Induction Heating (IH) jar. The proposed hybrid control scheme has characteristics, which acts as class-E inverter at lower switch voltage and ACCE inverter at higher switch voltage than reference voltage of the main switch by feeding back voltage of the switch. The proposedv hybrid control scheme also has advantage of conventional ACCE inverter such as Zero-Voltage-Switch(ZVS) of the main switch and the reduced switch voltage due to clamping cricuit. Moreover, the proposed hybrid control method using ACCE inverter has higher output power than convenional control scheme since ACCE inverter operates like class-E inverter at low input voltage condition. The principles of the proposed control are explained in detail and the validity of the proposed control scheme is verifed through the several interesting simulated and experimental results.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.52-60
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• 2022

Few-shot learning is a sub-area of machine learning problems, which aims to classify target images that only contain a few labeled samples for training. As a representative few-shot learning method, the Prototypical network has been received much attention due to its simplicity and promising results. However, the Prototypical network uses the sample mean of samples from the same class as the prototypes of that class, which easily results in learning uncharacteristic features in the low-data scenery. In this study, we propose to use local descriptors (i.e., patches along the channel within feature maps) from the same class to explicitly obtain more representative prototypes for Prototypical Network so that significant intra-class feature information can be maintained and thus improving the classification performance on few-shot learning tasks. Experimental results on various benchmark datasets including mini-ImageNet, CUB-200-2011, and tiered-ImageNet show that the proposed method can learn more discriminative intra-class features by the local descriptors and obtain more generic prototype representations under the few-shot setting.

• English Language & Literature Teaching
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• v.9 no.2
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• pp.71-90
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• 2003

The purpose of this study was to analyze the improvement of students' interest and general proficiency of English through cultural understanding. To achieve this purpose, two classes of the 2nd grade in the informational high school were divided into the experimental class and the control class. The Grammar-Translation Method was used for the control class and a cultural learning - compare and contrast Korean culture and American culture - was taken for the experimental. After various cultural differences were studied, surveys of students' attitude and reading and listening test were taken. The results from this study were as follows: Firstly, students' interest in English was improved through learning the American culture that was related to the content of each lesson. Secondly, English reading and communicative skills were improved by learning about cultural aspects. Therefore, it can be said that teaching culture stimulates students' interest and motivation for learning English and helps students retain such affective attitudes. And English communicative skills were improved as well.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.9-15
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• 2023

A common language for modeling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint-based approach that can be implemented along with an ordinary lab task. Some keywords are highlighted to indicate class diagram components and make students understand the textual descriptions. The experimental results indicate significant improvement in students' learning skills. Furthermore, the majority of students also positively responded to the survey conducted in the end experimental study.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.180-186
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• 2024

A common language for modelling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently in order to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint based approach that can be implemented along with an ordinary lab task. Some keywords are heighted to indicate class diagram component and make students to understand the textual descriptions. The experimental results indicate significant improvement in students learning skills. Furthermore, majority of students also positively responded to the survey conducted in the end experimental study.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.376-385
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• 2018

The purpose of this study was to investigate the effect of mobile task information presentation using 3D multimedia on tooth carving knowledge, performance and class satisfaction for dentistry. To accomplish this purpose, we divided 66 dental students into the two groups: The experimental group was presented with mobile task information using 3D modeling. and the control group was presented task information using textbook. As a result for the study, First, Mobile task information presentation using 3D multimedia in class has significant effect on the tooth carving performance and class satisfaction. Second, tooth morphology knowledge in control group presenting task information using the text book showed relatively higher than in experimental group which presenting mobile task information using the 3D modeling. The conclusion of this study is that the class presenting mobile task information using 3D modeling can be enhances class satisfaction and, teaching and learning strategy to improve tooth carving performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.179-186
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• 2008

In this paper we analyse the effects of Bayesian algorithm in teaming class imbalance problems and compare the performance evaluation methods. The teaming performance of the Bayesian algorithm is evaluated over the class imbalance problems generated by priori data distribution, imbalance data rate and discrimination complexity. The experimental results are calculated by the AUC(Area Under the Curve) values of both ROC(Receiver Operator Characteristic) and PR(Precision-Recall) evaluation measures and compared according to imbalance data rate and discrimination complexity. In comparison and analysis, the Bayesian algorithm suffers from the imbalance rate, as the same result in the reported researches, and the data overlapping caused by discrimination complexity is the another factor that hampers the learning performance. As the discrimination complexity and class imbalance rate of the problems increase, the learning performance of the AUC of a PR measure is much more variant than that of the AUC of a ROC measure. But the performances of both measures are similar with the low discrimination complexity and class imbalance rate of the problems. The experimental results show 4hat the AUC of a PR measure is more proper in evaluating the learning of class imbalance problem and furthermore gets the benefit in designing the optimal learning model considering a misclassification cost.

• ETRI Journal
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• v.30 no.5
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• pp.753-755
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• 2008

In this letter, we propose a new histogram equalization technique for feature compensation in speech recognition under noisy environments. The proposed approach combines a signal-to-noise-ratio-dependent feature reconstruction method and the class histogram equalization technique to effectively reduce the acoustic mismatch present in noisy speech features. Experimental results from the Aurora 2 task confirm the superiority of the proposed approach for acoustic feature compensation.