• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.192-201
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• 2002

Automated text classification is considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been addressed with machine learning technologies such as k-nearest neighbor, decision tree, support vector machine and neural networks. However, only few investigations in text classification are studied on real problems but on well-organized text corpus, and do not show their usefulness. This paper proposes and analyzes text classification methods for a real application, email document classification task. First, we propose a combining method of multiple neural networks that improves the performance through the combinations with maximum and neural networks. Second, we present another strategy of combining multiple machine learning classifiers. Voting, Borda count and neural networks improve the overall classification performance. Experimental results show the usefulness of the proposed methods for a real application domain, yielding more than 90% precision rates.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.27-34
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• 2008

Wireless sensor networks are comprised of sensor nodes with resource-constrained hardware. Nodes in the sensor network without adequate protection may be compromised by adversaries. Such compromised nodes are vulnerable to the attacks like false reports injection attacks and false data injection attacks on legitimate reports. In false report injection attacks, an adversary injects false report into the network with the goal of deceiving the sink or the depletion of the finite amount of energy in a battery powered network. In false data injection attacks on legitimate reports, the attacker may inject a false data for every legitimate report. To address such attacks, the probabilistic voting-based filtering scheme (PVFS) has been proposed by Li and Wu. However, each cluster head in PVFS needs additional transmission device. Therefore, this paper proposes a fuzzy logic-based false report detection method (FRD) to mitigate the threat of these attacks. FRD employs the statistical en-route filtering scheme as a basis and improves upon it. We demonstrate that FRD is efficient with respect to the security it provides, and allows a tradeoff between security and energy consumption, as shown in the simulation.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.949-958
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• 2004

race detection using a boosting based algorithm requires a very large size of face and nonface data. In addition, the fact that there always occurs a need for adding additional training data for better detection rates demands an efficient incremental teaming algorithm. In the design of incremental teaming based classifiers, the final classifier should represent the characteristics of the entire training dataset. Conventional methods have a critical problem in combining intermediate classifiers that weight updates depend solely on the performance of individual dataset. In this paper, for the purpose of application to face detection, we present a new method to combine an intermediate classifier with previously acquired ones in an optimal manner. Our algorithm creates a validation set by incrementally adding sampled instances from each dataset to represent the entire training data. The weight of each classifier is determined based on its performance on the validation set. This approach guarantees that the resulting final classifier is teamed by the entire training dataset. Experimental results show that the classifier trained by the proposed algorithm performs better than by AdaBoost which operates in batch mode, as well as by ${Learn}^{++}$.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.842-848
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• 2020

Music markets have entered the era of streaming. In order to select and propose music that suits the taste of music consumers, there is an active demand and research on an automatic music genre classification system. We propose a method to improve the accuracy of genre unclassified songs, which was a lack of the previous system, by using a generative adversarial network (GAN) to further develop the automatic voting system for deep learning music genre using Softmax proposed in the previous paper. In the previous study, if the spectrogram of the song was ambiguous to grasp the genre of the song, it was forced to leave it as an unclassified song. In this paper, we proposed a system that increases the accuracy of genre classification of unclassified songs by converting the spectrogram of unclassified songs into an easy-to-read spectrogram using GAN. And the result of the experiment was able to derive an excellent result compared to the existing method.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.401-410
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• 2021

Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.

• Survey Research
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• v.6 no.2
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• pp.51-74
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• 2005

The internet survey methods have been more and more widely used as the coverage of the fixed-line telephone is being reduced due to the diffusion of mobile phone. So, there is a need to know the accuracy of this new survey method. This study aims to estimate the accuracy of the internet survey method and identify the factors affecting the accuracy of this method, For this purpose, we analyzed the election poll data during the 17th general election period. These data include fixed-line telephone survey data, internet survey data, mobile phone survey data and the election voting data. The analysis shows that the prediction errors of the internet survey were a little more than those of the telephone or mobile phone survey. But the differences are not significant. It follows from this result that we can use the internet survey method in social survey context. This study also found that the respondent's willingness to participate in the survey, the probability of being at home during survey and the respondent's educational level were affecting the accuracy of the internet survey. Further studies to develop weighting method with these factors are needed.

• Survey Research
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• v.8 no.1
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• pp.55-79
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• 2007

This article attempts to provide an overview of the exit polls for the 2006 local elections in Korea. The sampling method, sampling error, non-response rate, and prediction error of the exit polls are reviewed. Also, we explore the fact that the propensity to vote varies according to age and gender of voters. In terms of age and gender, the representativeness of the sample is investigated by comparing to the data released by the National Election Commission. Through this empirical research, we show that the exit poll samples are unbalanced in terms of age and this unbalance may be one of the causes of bias occurred in the prediction of the 2006 local election results. The design effects of the sample design implemented for the exit polls are also examined.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.319-324
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• 2005

An integrated approach for prostate cancer detection using proteomic data is presented. Due to the high-dimensional feature of proteomic data, the discrete wavelet transform (DWT) is used in the first-stage for data reduction as well as noise removal. After the process of DWT, the dimensionality is reduced from 43,556 to 1,599. Thus, each sample of proteomic data can be represented by 1599 wavelet coefficients. In the second stage, a voting method is used to select a common set of wavelet coefficients for all samples together. This produces a 987-dimension subspace of wavelet coefficients. In the third stage, the Autoassociator algorithm reduces the dimensionality from 987 to 400. Finally, the artificial neural network (ANN) is applied on the 400-dimension space for prostate cancer detection. The integrated approach is examined on 9 categories of 2-class experiments, and also 3- and 4-class experiments. All of the experiments were run 10 times of ten-fold cross-validation (i. e. 10 partitions with 100 runs). For 9 categories of 2-class experiments, the average testing accuracies are between 81% and 96%, and the average testing accuracies of 3- and 4-way classifications are 85% and 84%, respectively. The integrated approach achieves exciting results for the early detection and diagnosis of prostate cancer.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.915-925
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• 1996

In this paper, in order to find effective features which can handle variations in off-line handwritten numerals, we performed a comparative study on various sets of features. Results of experimental performance comparison shows that 4- directional features using contours and features which combined cross distance, cross, mesh and projection features are very effective for off-line handwritten numerals recognition in terms of recognition rates and recognition time. And in order to surmount limitation of recognition rate by a single neural network. we proposed a modularized neural network using majority voting and reliability factor with complex feature that mix effective features together. In order to verify the performance of the proposed method, the handwritten numeral databases of Concordia University of Canada and Dong-A University of Korea are used in the experiments. With the database of Concordia University, the recognition rate of 97.1%, the rejection rate of 1.5%, the error rate of 1.4% and the reliability of 98.5% are obtained ; and with the database of Dong-A University, there cognition rate of 98%, the rejection rate of 1.2%, the error rate of 0.8%, the reliability o99.1% are obtained.