• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1557-1563
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• 2015

Multiclass classification is typically performed using the voting scheme method based on combining binary classifications. In this paper we propose multiclass classification method for large data, which can be regarded as the revised one-vs-all method. The multiclass classification is performed by using the hat matrix of least squares support vector machine (LS-SVM) ensemble, which is obtained by aggregating individual LS-SVM trained on each subset of whole large data. The cross validation function is defined to select the optimal values of hyperparameters which affect the performance of multiclass LS-SVM proposed. We obtain the generalized cross validation function to reduce computational burden of cross validation function. Experimental results are then presented which indicate the performance of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.7
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• pp.311-316
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• 2019

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.399-401
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• 2003

This paper presents a geostatistical contextual classifier for the classification of remote sensing data. To obtain accurate spatial/contextual information, a simple indicator kriging algorithm with local means that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is applied. To illustrate the proposed scheme, supervised classification of multi-sensor remote sensing data is carried out. Analysis of the results indicates that the proposed method improved the classification accuracy, compared to the method based on the spectral information only.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.3
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• pp.138-142
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• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.151-171
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• 2012

In applying RFID/USN technology to various industries, it is needed to solve the problem caused by the system differences. Accordingly, this study introduces the common standard platform concept, and suggests the standard data scheme which provides the uniform perspective of classifying supply chain data and of using vocabularies. We selected several industry areas applicable for the platform, which are pharmaceutical, cosmetics, food and liquor industry. We collect and organize terminologies used in the supply chain of each industry, and then classify them according to the defined data attributes. The standardized vocabularies are suggested based on the contextured scheme of data classification. This study could provide more convenient way of communication between business partners, system developers and users of the platform.

• Journal of The Geomorphological Association of Korea
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• v.19 no.3
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• pp.1-22
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• 2012

The classification scheme of estuaries can be divided into two categories: qualitative classification based on geomorphic characteristics and quantitative classification based upon the physical properties of water body. While simple and intuitive scheme of the former is difficult to quantify, the latter is not easy to apply due to the lack of data. A classification scheme based on morphological convergence is very promising because it only requires easily accessible data such as width and depth of channels, as well as it can characterize estuaries in terms of tidal propagation. Thus, this paper examines the classification scheme based on estuarine morphological convergence using depth and width data obtained from 19 major Korean estuaries. Morphological convergence for each estuary was estimated with the estuarine length, width and depth data to get the convergence parameters, which includes the degree of funneling ${\nu}$ and the dimensionless estuarine length $y_0$. The transfer function ${\xi}({\nu},ky)$ is then deduced analytically from 1D depth-integrated hydrodynamic momentum equation and continuity equation for estuarine shapes. Tidal response of each estuary is finally calculated using ${\nu}$, $y_0$ and ${\xi}({\nu},ky)$ for comparison and classification. The 19 Korean estuaries were classified into three groups: tidal amplitude-dominated estuaries with standing wave-like tidal response (group 1), current-dominated estuaries with progressive wave-like tidal response (group 2), and the intermediate group (group 3) between groups 1 and 2. The sensitivity analysis revealed that uncertainties in determining the estuarine length can have a critical effect upon the results of classification, which indicates that the reasonable determination of the estuarine length is of critical importance. Once the estuarine length is feasibly determined, depth-convergence can be neglected without any negative effect on the classification scheme, which has an important ramification on the wide applicability of the classification scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.229-232
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• 2008

A Neural network classification of human activity data is presented. The data acquisition system involves a tri-axial accelerometer in wireless sensor network environment. The wireless ad-hoc system has the advantage of small size, convenience for wearability and cost effectiveness. The system can further improve the range of user mobility with the inclusion of ad-hoc environment. The classification is based on the frequencies of the involved activities. The most significant Fast Fourier coefficients, of the acceleration of the body movement, are used for classification of the daily activities like, Rest walk and Run. A supervised learning approach is used. The work presents classification accuracy with the available fast batch training algorithms i.e. Levenberg-Marquardt and Resilient back propagation scheme is used for training and calculation of accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.15-25
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• 2024

For effective analysis of animal ecosystems, technology that can automatically identify the current status of animal habitats is crucial. Specifically, animal sound classification, which identifies species based on their sounds, is gaining great attention where video-based discrimination is impractical. Traditional studies have relied on a single deep learning model to classify animal sounds. However, sounds collected in outdoor settings often include substantial background noise, complicating the task for a single model. In addition, data imbalance among species may lead to biased model training. To address these challenges, in this paper, we propose an animal sound classification scheme that combines predictions from multiple models using Focal Loss, which adjusts penalties based on class data volume. Experiments on public datasets have demonstrated that our scheme can improve recall by up to 22.6% compared to an average of single models.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.49.1-49.1
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• 2016

Asteroid taxonomy dates back to the mid-1970's and is based mostly on broadband photometric and spectroscopic observations in the visible wavelength. Different taxonomic classes have long been characterized by spectral slope shortward of 0.75 microns and the absorption band in 1 micron, the principal components. In this way, taxonomic classes are grouped and divided into four broad complexes; silicates (S), carbonaceous (C), featureless (X), Vestoids (V), and the end-members that do not fit well within the S, C, X and V complexes. The past decade witnessed an explosion of data due to the advent of large-scale asteroid surveys such as SDSS. The classification scheme has recently been expanded with the analysis of the SDSS 4th Moving Object Catalog (MOC 4) data. However, the boundaries of each complex and subclass are rather ambiguously defined by hand. Furthermore, there are only few studies on asteroid taxonomy using Johnson-Cousins filters, and those were conducted on a small number of objects, with significant uncertainties. In this paper, we present our preliminary results for a new taxonomic classification of asteroids using SMASS, Bus and DeMeo (2014) and the SDSS MOC 4 datasets. This classification scheme is simply represented by a triplet of photometric colors, either in SDSS or in Johnson-Cousins photometric systems.

• Journal of Soil and Groundwater Environment
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• v.15 no.6
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• pp.64-71
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• 2010

National inventory of soil and groundwater contamination is an efficient decision-making tool to identify and manage existing or potential contaminated sources and contaminants. It has been used as basic data for establishing the scheme of regulations and remediation plans of soil and groundwater contamination in developed countries. This study examined classification of existing or potential sources of soil and groundwater contamination from various countries to suggest implications that required for development of classification of soil and groundwater contamination sources in Korea. Each country has provided a list of currently or potentially contaminating activities or landuses and identified some of the potential contaminants related to those contamination sources. Consideration of sources which had not been mentioned or regarded as contamination sources before was suggested for Korea situation. In addition, it is necessary to compile a list of existing data and information as much as possible to develop a detailed and practical list of various contamination sources.