• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.509-520
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• 2005

Data fusion is defined as the process of combining data and information from different sources for the effectiveness of the usage of useful information contents. In this paper, we propose a data fusion algorithm using k-means clustering method for data enrichment to improve data quality in knowledge discovery in database(KDD) process. An empirical study was conducted to compare the proposed data fusion technique with the existing techniques and shows that the newly proposed clustering data fusion technique has low MSE in continuous fusion variables.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.545-553
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• 2015

While all of the data has a value in itself, most of the data that is collected in the real world is a random and unstructured. In order to extract useful information from the data, it is need to use the data transform and analysis algorithms. Data mining is used for this purpose. Today, there is not only need for a variety of data mining techniques to analyze the data but also need for a computational requirements and rapid analysis time for huge volume of data. The method commonly used to store huge volume of data is to use the hadoop. A method for analyzing data in hadoop is to use the MapReduce framework. In this paper, we developed a tournament-based MapReduce method for high efficiency in developing an algorithm on a single machine to the MapReduce framework. This proposed method can apply many analysis algorithms and we showed the usefulness of proposed tournament based method to apply frequently used data mining algorithms k-means and k-nearest neighbor classification.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.417-426
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• 2018

An alternative developmental toxicity test using mouse embryonic stem cell derived embryoid bodies has been developed. This alternative method is not to administer chemicals to animals, but to treat chemicals with cells. This study suggests the use of Discriminant Analysis, Support Vector Machine, Artificial Neural Network and k-Nearest Neighbor. Algorithm performance was compared with accuracy and a weighted Cohen's kappa coefficient. In application, various classification techniques were applied to cytotoxicity data to classify drug toxicity and compare the results.

• Journal of the Korea Society for Simulation
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• v.20 no.2
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• pp.77-85
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• 2011

It is believed that the anomalies or diseases of human organs are identified by the analysis of the patterns. This paper proposes a new classification technique for the identification of cancer disease using the proteome patterns obtained from two-dimensional polyacrylamide gel electrophoresis(2-D PAGE). In the new classification method, three different classification methods such as support vector machine(SVM), multi-layer perceptron(MLP) and k-nearest neighbor(k-NN) are extended by multi-boosting method in an array of subclassifiers and the results of each subclassifier are merged by ensemble method. Genetic algorithm was applied to obtain optimal feature set in each subclassifier. We applied our method to empirical data set from cancer research and the method showed the better accuracy and more stable performance than single classifier.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.12-16
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• 2007

The function of a protein is closely co-related with its subcellular location(s). Given a protein sequence, therefore, how to determine its subcellular location is a vitally important problem. We have developed a new prediction method for protein subcellular location(s), which is based on n-gram feature extraction and k-nearest neighbor (kNN) classification algorithm. It classifies a protein sequence to one or more subcellular compartments based on the locations of top k sequences which show the highest similarity weights against the input sequence. The similarity weight is a kind of similarity measure which is determined by comparing n-gram features between two sequences. Currently our method extract penta-grams as features of protein sequences, computes scores of the potential localization site(s) using kNN algorithm, and finally presents the locations and their associated scores. We constructed a large-scale data set of protein sequences with known subcellular locations from the SWISS-PROT database. This data set contains 51,885 entries with one or more known subcellular locations. Our method show very high prediction precision of about 93% for this data set, and compared with other method, it also showed comparable prediction improvement for a test collection used in a previous work.

• Journal of Advanced Navigation Technology
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• v.1 no.1
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• pp.35-46
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• 1997

Over last decade Multiple Target Tracking (MTT) has been the subject of numerous presentations and conferences [1979-1900]. Various approaches have been proposed to solve the problem. Representative works in the problem are Nearest Neighbor (NN) method based on non-probabilistic data association (DA), Multiple Hypothesis Test (MHT) and Joint Probabilistic Data Association (JPDA) as the probabilistic approaches. These techniques have their own advantages and limitations in computational requirements and in the tracking performances. In this paper, the three promising algorithms based on the NN standard filter, MHT and JPDA methods are presented and their performances against simulated multiple maneuvering targets are compared through numerical simulations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.115-124
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• 2005

This paper analyzes the problems that occurred in the magnification process for a fine input image and investigates a method to improve the problems. This paper applies a curve interpolation algorithm in CAD/CAM for the same test images with the existing image algorithm in order to improve the problems. As a result. the nearest neighbor interpolation. which is the most frequently applied algorithm for the existing image interpolation algorithm. shows that the identification of a magnified image is not possible. Therefore. this study examines an interpolation of gray-level data by applying a low-pass spatial filter and verifies that a bilinear interpolation presents a lack of property that accentuates the boundary of the image where the image is largely changed. The periodic B-spline interpolation algorithm used for curve interpolation in CAD/CAM can remove the blurring but shows a problem of obscuration, and the Ferguson's curve interpolation algorithm shows a more sharpened image than that of the periodic B-spline algorithm. For the future study, hereafter. this study will develop an interpolation algorithm that has an excel lent improvement for the boundary of the image and continuous and flexible property by using the NURBS. Ferguson's complex surface. and Bezier surface used in CAD/CAM engineering based on. the results of this study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.785-796
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• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.