• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.824-830
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• 2003

RPA (Recursive Partition Averaging) method was proposed in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked well in many areas, however, the major drawbacks of RPA are it's pattern averaging mechanism. We propose an adaptive OHC algorithm which uses the FPD(Feature-based Population Densimeter) to increase the classification rate of RPA. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed a excellent results in terms of classification when we compare it to the k-NN.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.478-487
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• 2004

We had proposed the RPA(Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked not bad in many area, however, the major drawbacks of RPA are it's partitioning condition and the way of extracting major patterns. We propose an adaptive RPA algorithm which uses the FPD(feature-based population densimeter) to stop the ARPA partitioning process and produce, instead of RPA's averaged major pattern, optimizing resulting hyperrectangles. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed an excellent results in terms of classification when we compare it to the k-NN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.317-319
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• 2021

Throughout the era of the fourth industrial revolution, automation is becoming more important. Recently, business automation solutions using Robotic Process Automation (RPA) are also attracting attention. Compared to the rapidly growing RPA market, related IT technologies have not been widely available and problems such as manpower shortage are growing. Therefore, this study identifies the definition and characteristics of RPA, classification by type of operation, and the impact of RPA solutions and introduction, which have significant impact on the enterprise in a short period of time. In addition, we present the development direction of RPA through implications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1901-1906
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• 2010

This classification of walking patterns is important and many kinds of applications. Therefore, we attempted to classify walking on level ground from slow walking to fast walking using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by bluetooth module at a sampling rate of 100 Hz eleven healthy. The data were analyzed using discrete wavelet transform. Walking patterns were classified using two parameters; One was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction(RAV). Slow walking could be distinguished by the smallest value in RPA from other walking pattern. Fast walking could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signal in healthy people.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.11-17
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• 2007

One of the popular methods used for pattern classification is the MBR (Memory-Based Reasoning) algorithm. Since it simply computes distances between a test pattern and training patterns or hyperplanes stored in memory, and then assigns the class of the nearest training pattern, it cannot explain how the classification result is obtained. In order to overcome this problem, we propose an incremental teaming algorithm based on RPA (Recursive Partition Averaging) to extract IF-THEN rules that describe regularities inherent in training patterns. But rules generated by RPA eventually show an overfitting phenomenon, because they depend too strongly on the details of given training patterns. Also RPA produces more number of rules than necessary, due to over-partitioning of the pattern space. Consequently, we present the IREA (Incremental Rule Extraction Algorithm) that overcomes overfitting problem by removing useless conditions from rules and reduces the number of rules at the same time. We verify the performance of proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Journal of Korean Neurosurgical Society
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• v.46 no.3
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• pp.245-251
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• 2009

Objective : The authors analyzed the effectiveness and therapeutic response of Novalis shaped beam radiosurgery for metastatic brain tumors, and the prognostic factors which influenced the outcome. Methods : We performed a retrospective analysis of 106 patients who underwent 159 treatments for 640 metastatic brain lesions between January 2000 and April 2008. The pathologies of the primary tumor were mainly lung (45.3%), breast (18.2%) and GI tract (13.2%). We classified the patients using Radiation Therapy Oncology Group Recursive Partitioning Analysis (RPA) and then analyzed the survival and prognostic factors according to the Kaplan Meier method and univariate analysis. Results : The overall median actuarial survival rate was 7.3 months from the time of first radiosurgery treatment while 1 and 2 year actuarial survival estimates were 31% and 14.4%, respectively. Median actuarial survival rates for RPA classes I, II, and III were 31.3 months, 7.5 months and 1.7 months, respectively. Patients' life spans, higher Karnofsky performance scores and age correlated closely with RPA classes. However, sex and the number of lesions were not found to be significantly associated with length of survival. Conclusion : This result suggests that Novalis radiosurgery can be a good treatment option for treatment of the patients with brain metastases.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1849-1857
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• 1999

We proposed the RPA (Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. This algorithm recursively partitions the pattern space until each hyperrectangle contains only those patterns of the same class, then it computes the average values of patterns in each hyperrectangle to extract a representative. Also we have used the mutual information between the features and classes as weights for features to improve the classification performance. The proposed algorithm used 30~90% of memory space that is needed in the k-NN (k-Nearest Neighbors) classifier, and showed a comparable classification performance to the k-NN. Also, by reducing the number of stored patterns, it showed an excellent result in terms of classification time when we compare it to the k-NN.

• The Journal of Korean Academy of Prosthodontics
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• v.48 no.3
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• pp.189-193
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• 2010

Purpose: This study was aimed to investigate the frequency of different classes of partial edentulism and the most frequently used design components of conventional removable partial dentures. Materials and methods: 63 patients who were treated with removable partial denture in Kyungpook National University hospital for 2003-2006 were selected. A total of 76 removable partial denture frameworks were investigated. Kennedy classification was used to identify the class of partial edentulism. Results: Results indicated that Kennedy class I removable partial dentures were the most frequently constructed. Most patients' cases were designed without modification areas. Conclusion: The most common type of direct retainer were the RPI clasp and RPA clasp in both maxilla and mandible. Lingual bar, linguoplate and anterior posterior palatal straps were the more frequently used mandibular and maxillary major connectors respectively. We did not have any case about Kennedy class IV patients.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.461-466
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• 2024

Korean local governments operates the participatory budgeting system autonomously. This study is to classify these entities into clusters. Among the diverse machine learning methodologies(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes), the Support Vector Machine technique emerged as the most efficacious in the analysis of 2022 Korean municipalities data. The first cluster C1 is characterized by minimal committee activity but a substantial allocation of participatory budgeting; another cluster C3 comprises cities that exhibit a passive stance. The majority of cities falls into the final cluster C2 which is noted for its proactive engagement in. Overall, most Korean local government operates the participatory busgeting system in good shape. Only a small number of cities is less active in this system. We anticipate that analyzing time-series data from the past decade in follow-up studies will further enhance the reliability of classifying local government types regarding participatory budgeting.