• Journal of Multimedia Information System
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• 제8권4호
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• 산업기술연구
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• 제19권
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• pp.403-408
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• 1999

An efficient method is developed for classifying fingerprint data based on 2-D discrete wavelet transform. Fingerprint data is first converted to a binary image. Then a multi-level 2-D wavelet transform is performed. Vertical and horizontal subbands of the transformed data show typical energy distribution patterns relevant to the fingerprint categories. The proposed method with moderate level of wavelet transform is successful in classifying fingerprints into 5 different types. Finer classification is possible by higher frequency subbands and closer analysis of energy distribution.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.129-133
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• 2023

Classification or prediction problem is how to solve it using a specific feature to obtain the predicted class. A wheat seeds specifications 4 3 classes of seeds will be used in a prediction process. A multi linear regression will be built, and a prediction error ratio will be calculated. To enhance the prediction ratio an ANN model will be built and trained. The obtained results will be examined to show how to make a prediction tool capable to compute a predicted class number very close to the target class number.

• 한국음향학회지
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• 제30권8호
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• pp.446-460
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• 2011

최근 항공 산업, 자동차 산업 등의 산업 현장에서 유도 전동기의 사용이 증대되고 있으며, 유도 전동기는 산업 현장에서 중요한 역할을 하고 있다. 따라서 유도 전동기의 고장으로 인한 피해를 최소화하기 위해 유도 전동기의 고장 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 이와 같은 이유로 본 논문에서는 유도 전동기의 고장을 조기에 검출하고 진단하기 위해 에너지 (short-time energy)와 특이치 분해와 이산 코사인 변환과 특이치 분해를 이용한 특징 벡터 추출 방법을 제안하였고, 이를 역 전파 신경 회로망과 다층 서포트 벡터 머신의 입력으로 이용하여 유도 전동기의 고장을 유형별로 분류하였다. 하지만 본 논문에서는 역 전파 신경 회로망과 다층 서포트 벡터 머신을 분류기로 사용함에 있어 역 전파 신경 회로망은 신경망을 구성하는 입력 뉴런 수, 은닉 뉴런 수, 학습 알고리즘에 의해 분류 성능이 달라지며, 다층 서포트 벡터 머신은 커널 함수로 사용한 가우시안 방사 기저 함수의 표준 편차 값에 따라 분류 성능이 달라지는 점을 고려하여 여러 가지 조건하에서의 실험을 통해 높은 분류 성능을 보이는 설정 방법을 제시하였다.

• Asia pacific journal of information systems
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• 제20권2호
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• pp.23-37
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• 2010

Conventional Support Vector Machines (SVMs) have been utilized as classifiers for binary classification problems. However, certain real world problems, including corporate bond rating, cannot be addressed by binary classifiers because these are multi-class problems. For this reason, numerous studies have attempted to transform the original SVM into a multiclass classifier. These studies, however, have only considered nominal classification problems. Thus, these approaches have been limited by the existence of multiclass classification problems where classes are not nominal but ordinal in real world, such as corporate bond rating and multiclass customer classification. In this study, we adopt a novel multiclass SVM which can address ordinal classification problems using ordinal pairwise partitioning (OPP). The proposed model in our study may use fewer classifiers, but it classifies more accurately because it considers the characteristics of the order of the classes. Although it can be applied to all kinds of ordinal multiclass classification problems, most prior studies have applied it to finance area like bond rating. Thus, this study applies it to a real world customer level classification case for implementing customer relationship management. The result shows that the ordinal multiclass SVM model may also be effective for customer level classification.

• 대한원격탐사학회지
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• 제23권3호
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• pp.153-160
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• 2007

Forest cover density studies using high resolution satellite data and object oriented classification are limited in India. This article focuses on the potential use of QuickBird satellite data and object oriented classification in forest density mapping. In this study, the high-resolution satellite data was classified based on NDVI/pixel based and object oriented classification methods and results were compared. The QuickBird satellite data was found to be suitable in forest density mapping. Object oriented classification was superior than the NDVI/pixel based classification. The Object oriented classification method classified all the density classes of forest (dense, open, degraded and bare soil) with higher producer and user accuracies and with more kappa statistics value compared to pixel based method. The overall classification accuracy and Kappa statistics values of the object oriented classification were 83.33% and 0.77 respectively, which were higher than the pixel based classification (68%, 0.56 respectively). According to the Z statistics, the results of these two classifications were significantly different at 95% confidence level.

• 디지털융복합연구
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• 제19권12호
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• pp.347-352
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• 2021

본 논문은 다중 에이전트 강화학습 방식을 사용하여 입력 데이터로부터 분류에 효과적인 특징 집합을 찾아내는 방식을 제안한다. 기계 학습 분야에 있어서 분류에 적합한 특징들을 찾아내는 것은 매우 중요하다. 데이터에는 수많은 특징들이 존재할 수 있으며, 여러 특징들 중 일부는 분류나 예측에 효과적일 수 있지만 다른 특징들은 잡음 역할을 함으로써 올바른 결과를 생성하는 데에 오히려 악영향을 줄 수 있다. 기계 학습 문제에서 분류나 예측 정확도를 높이기 위한 특징 선택은 매우 중요한 문제 중 하나이다. 이러한 문제를 해결하기 위해 강화학습을 통한 특징 선택 방법을 제시한다. 각각의 특징들은 하나의 에이전트를 가지게 되며, 이 에이전트들은 특징을 선택할 것인지 말 것인지에 대한 여부를 결정한다. 에이전트들에 의해 선택된 특징들과 선택되지 않은 특징들에 대해서 각각 보상을 구한 뒤, 보상에 대한 비교를 통해 에이전트의 Q-value 값을 업데이트 한다. 두 하위 집합에 대한 보상 비교는 에이전트로 하여금 자신의 행동이 옳은지에 대한 판단을 내릴 수 있도록 도와준다. 이러한 과정들을 에피소드 수만큼 반복한 뒤, 최종적으로 특징들을 선별한다. 이 방법을 통해 Wisconsin Breast Cancer, Spambase, Musk, Colon Cancer 데이터 세트에 적용한 결과, 각각 0.0385, 0.0904, 0.1252, 0.2055의 정확도 향상을 보여주었으며, 최종적으로 0.9789, 0.9311, 0.9691, 0.9474의 분류 정확도를 보여주었다. 이는 우리가 제안한 방법이 분류에 효과적인 특징들을 잘 선별하고 분류에 대한 정확도를 높일 수 있음을 보여준다.

• Journal of Information Technology Applications and Management
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• 제14권2호
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.