• Journal of the Korea Society for Simulation
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• v.21 no.2
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• pp.79-90
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• 2012

In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling's $T^2$ Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.99-104
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• 2011

This paper presents a high power and high efficiency unbalanced Doherty power amplifier used to extend the output power back-off (OPBO). The proposed unbalanced amplifier uses the same type of transistors in both the main amplifier and the peaking amplifier, similar to a conventional symmetric Doherty amplifier. The unbalanced amplifier can have the impedance of a ${\lambda}/4$ transformer located at the output of the main amplifier modified. This enables the OPBO to exceed 6 dB, the maximum OPBO for a conventional symmetric Doherty amplifier. The efficiency and linearity performance of the unbalanced Doherty amplifier are almost same as those found for the asymmetric Doherty amplifier, even though the unbalanced Doherty amplifier structure is simpler than the asymmetric Doherty structure. In order to verify the proposed amplifier performance, a 46 W Doherty amplifier has been both simulated and measured using a CDMA2000 1FA signal. From the measured results, the proposed unbalanced Doherty amplifier achieved an added power efficiency of 38 % and an adjacent channel power ratio of -34 dBc at a 885 kHz offset frequency and -35.6 dBc at a 1.98 MHz offset frequency.

• KIISE Transactions on Computing Practices
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• v.20 no.10
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• pp.549-554
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• 2014

Handwritten character recognition data is usually imbalanced because it is collected from the natural language sentences written by different writers. The imbalanced data can cause seriously negative effect on the performance of most of machine learning algorithms. But this problem is typically ignored in handwritten character recognition, because it is considered that most of difficulties in handwritten character recognition is caused by the high variance in data set and similar shapes between characters. We propose the oversampling-based ensemble learning methods to solve imbalanced data problem in handwritten character recognition and to improve the recognition accuracy. Also we show that proposed method achieved improvements in recognition accuracy of minor classes as well as overall recognition accuracy empirically.

• Land and Housing Review
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• v.4 no.4
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• pp.371-382
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• 2013

This study performs an experimental investigation to evaluate the behavior of RC flat plate interior joints specimens. Three 60 percent scale Flat Plate interior specimens assemblies representing a portion of a Flat Plate Apartment Structural System subjected to simulated seismic loading (unbalanced moments) under constant axial load were tested, including one specimens with ordinary shear reinforcement and two specimens with folded bend type shear reinforcement. Test results are shown that (1) the design code KBC 2009 is accurate estimate the behavior of specimens. (2) Two types shear reinforcement have a similar structural behavior, but construction work of rebar with folded bend type shear reinforcement is easier than that of ordinary shear reinforcement. (3) In moderate seismic region, RC Flat Plate interior joint with folded bend type shear reinforcement is apply to structural design of Flat Plate.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.86-97
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• 2014

Incheon Free Economic Zone is the first free economic zone specified in 2003 in Korea. Previous research on the Free Economic Zone has focused on the activation of the free economic zone or foreign investment issue at the level of the national economic plan. Related to the development of a free economic zone, studies are currently insufficient on the relevance of the local economy, the development of linkages with hinterland, and the balanced regional development. Therefore, this study tried to investigate the impact of Incheon Free Economic Zone to the local economy through analyzing the causes and characteristics of the imbalance between the regions in Incheon by comparing before (1996~2002) and after (2003~2009) of the Incheon Free Economic Zone legislation (2003). The result shows that development of the free economic zone has not been connected to the local economy activation and the ripple effect on the old town in Incheon. Further, the construction of a large apartment and infrastructure in the free economic zones have increased the disparity between the free zones and the old town.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.9-21
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• 2015

The support vector machine has been successfully applied to various classification areas due to its flexibility and a high level of classification accuracy. However, when analyzing imbalanced data with uneven class sizes, the classification accuracy of SVM may drop significantly in predicting minority class because the SVM classifiers are undesirably biased toward the majority class. The weighted $L_2$-norm SVM was developed for the analysis of imbalanced data; however, it cannot identify irrelevant input variables due to the characteristics of the ridge penalty. Therefore, we propose the weighted $L_1$-norm SVM, which uses lasso penalty to select important input variables and weights to differentiate the misclassification of data points between classes. We demonstrate the satisfactory performance of the proposed method through simulation studies and a real data analysis.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.599-609
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• 2021

Data with a large difference in the number of objects between clusters are called unbalanced data. In discriminant analysis of unbalanced data, it is more important to classify objects in minority categories than to classify objects in majority categories well. However, objects in minority categories are often misclassified into majority categories. In this study, we propose a method that combined hierarchical DBSCAN (HDBSCAN) and SMOTE to solve this problem. Using HDBSCAN, it removes noise in minority categories and majority categories. Then it applies SMOTE to create new data. Area under the roc curve (AUC) and F1 scores were used to compare performance with existing methods. As a result, in most cases, the method combining HDBSCAN and synthetic minority oversampling technique (SMOTE) showed a high performance index, and it was found to be an excellent method for classifying unbalanced data.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.711-721
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• 2021

In this paper, we propose the method of adjusting thresholds using impurity indices in classification analysis on imbalanced data. Suppose the minority category is Positive and the majority category is Negative for the imbalanced binomial data. When categories are determined based on the commonly used 0.5 basis, the specificity tends to be high in unbalanced data while the sensitivity is relatively low. Increasing sensitivity is important when proper classification of objects in minority categories is relatively important. We explore how to increase sensitivity through adjusting thresholds. Existing studies have adjusted thresholds based on measures such as G-Mean and F1-score, but in this paper, we propose a method to select optimal thresholds using the chi-square statistic of CHAID, the Gini index of CART, and the entropy of C4.5. We also introduce how to get a possible unique value when multiple optimal thresholds are obtained. Empirical analysis shows what improvements have been made compared to the results based on 0.5 through classification performance metrics.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.49-54
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• 2021

With the recent development of smart healthcare technology, interest in daily diseases is increasing. However, healthcare data has an imbalance between positive and negative data. This is caused by the difficulty of collecting data because there are relatively many people who are not patients compared to patients with certain diseases. Data imbalances need to be adjusted because they affect performance in ongoing learning during disease prediction and analysis. Therefore, in this paper, We replace missing values through multiple imputation in detection models to determine whether they are prevalent or not, and resolve data imbalances through over-sampling. Based on AutoML using preprocessed data, We generate several models and select top 3 models to generate ensemble models.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.477-489
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• 2021

This study was conducted to confirm whether organization culture unbalance plays a role as a moderating variable on the mediation process that protean career orientation influences subjective career success through continuous learning activity. To this end, a survey was carried out on 276 office workers with more than 5 years of work experience in large companies, and the data were analyzed using SPSS 25 and Process Macro v3.5. The results showed that continuous learning activity mediates the relationship of protean career orientation affecting subjective career success, but moderating effect of organizational culture unbalance and the moderated mediation effect were not statistically significant. However, statistical significance was found on the moderating effect of organizational culture unbalance on the mediation process, that 'self-direction', protean career orientation's sub-factor, affects subjective career success and its' sub-factor 'employability', and 'career satisfaction' through continuous learning activity. The significance and limitations of our findings are also discussed.