Browsing by Author "Kuo, Bor-Chen"

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A Dynamic Subspace Method for Hyperspectral Image Classification
(2010-07-01)
Yang, Jinn-Min
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Kuo, Bor-Chen
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Yu, Pao-Ta
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Chuang, Chun-Hsiang
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電控工程研究所
Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles.
A NOVEL CLASSIFICATION PROCESSING BASED ON THE SPATIAL INFORMATION AND THE CONCEPT OF ADABOOST FOR HYPERSPECTRAL IMAGE CLASSIFICATION
(2010)
Kuo, Bor-Chen
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Lin, Shih-Syun
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Wu, Huey-Min
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Chuang, Chun-Hsiang
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電控工程研究所
In this paper, a novel classification processing based on the spatial information and the concept of Adaboost for hyperspectral image classification is proposed. This classification process is named as adaptive feature extraction with spatial information (AdaFESI). The main idea is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by spectral or spatial classifiers in the previous feature space. All training samples are projected into these feature spaces to train various classifiers and then constitute a multiple classifier system. The experimental results based on two hyperspectral data sets show that the proposed algorithm can generate better classification results.
A Semisupervised Feature Extraction Method Based on Fuzzy-type Linear Discriminant Analysis
(2011)
Chu, Hui-Shan
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Li, Cheng-Hsuan
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Kuo, Bor-Chen
;
Lin, Chin-Teng
;
電控工程研究所
Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, a semisupervised feature extraction method which is based on the scatter matrices of the fuzzy-type LDA and uses the semi-information is proposed. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.
A Spatial-Contextual Support Vector Machine for Remotely Sensed Image Classification
(2012-03-01)
Li, Cheng-Hsuan
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Kuo, Bor-Chen
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Lin, Chin-Teng
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Huang, Chih-Sheng
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電控工程研究所
Recent studies show that hyperspectral image classification techniques that use both spectral and spatial information are more suitable, effective, and robust than those that use only spectral information. Using a spatial-contextual term, this study modifies the decision function and constraints of a support vector machine (SVM) and proposes two kinds of spatial-contextual SVMs for hyperspectral image classification. One machine, which is based on the concept of Markov random fields (MRFs), uses the spatial information in the original space (SCSVM). The other machine uses the spatial information in the feature space (SCSVMF), i.e., the nearest neighbors in the feature space. The SCSVM is better able to classify pixels of different class labels with similar spectral values and deal with data that have no clear numerical interpretation. To evaluate the effectiveness of SCSVM, the experiments in this study compare the performances of other classifiers: an SVM, a context-sensitive semisupervised SVM, a maximum likelihood (ML) classifier, a Bayesian contextual classifier based on MRFs (ML_MRF), and k nearest neighbor classifier. Experimental results show that the proposed method achieves good classification performance on famous hyperspectral images (the Indian Pine site (IPS) and the Washington, DC mall data sets). The overall classification accuracy of the hyperspectral image of the IPS data set with 16 classes is 95.5%. The kappa accuracy is up to 94.9%, and the average accuracy of each class is up to 94.2%.
An Adaptive Rule Based on Unknown Pattern for Improving K-Nearest Neighbor Classifier
(2010-01-01)
Chen, I-Ling
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Pai, Kai-Chih
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Kuo, Bor-Chen
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Li, Cheng-Hsuan
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電控工程研究所
One of popular and simple pattern classification algorithms is the k-nearest neighbor rule. However, it often fails to work well when patterns of different classes overlap in some regions in the feature space. To overcome this problem, many researches strive for developing various adaptive or discriminatory metrics to improve its performance for classification, recently. In this paper, we proposed a simple adaptive nearest neighbor rule on distance measure for two objects. First one is to separate the overlapping data, and the second one is to avoid the influence of outliers. From the experimental results, our method is robust for the choice of the number of k and outperforms than k-nearest neighbor classifier.
An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines
(2012-01-01)
Li, Cheng-Hsuan
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Ho, Hsin-Hua
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Liu, Yu-Lung
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Lin, Chin-Teng
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Kuo, Bor-Chen
Soft-margin support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the normalized kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding soft-margin SVMs can obtain more accurate or at least equal performance than the soft-margin SVMs by applying k-fold cross-validation to determine the parameters.
An Automatic Method for Selecting the Parameter of the Normalized Kernel Function to Support Vector Machines
(2010-01-01)
Li, Cheng-Hsuan
;
Lin, Chin-Teng
;
Kuo, Bor-Chen
;
Ho, Hsin-Hua
;
電控工程研究所
Support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the normalized kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.
AN AUTOMATIC METHOD FOR SELECTING THE PARAMETER OF THE RBF KERNEL FUNCTION TO SUPPORT VECTOR MACHINES
(2010)
Li, Cheng-Hsuan
;
Lin, Chin-Teng
;
Kuo, Bor-Chen
;
Chu, Hui-Shan
;
電控工程研究所
Support vector machine (SVM) is one of the most powerful techniques for supervised classification. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. It is extremely time consuming by applying the k-fold cross-validation (CV) to choose the almost best parameter. Nevertheless, the searching range and fineness of the grid method should be determined in advance. In this paper, an automatic method for selecting the parameter of the RBF kernel function is proposed. In the experimental results, it costs very little time than k-fold cross-validation for selecting the parameter by our proposed method. Moreover, the corresponding SVMs can obtain more accurate or at least equal performance than SVMs by applying k-fold cross-validation to determine the parameter.
An EEG-based Classification System of Passenger's Motion Sickness Level by using Feature Extraction/Selection Technologies
(2010)
Yu, Yi-Hsin
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Lai, Pei-Chen
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Ko, Li-Wei
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Chuang, Chun-Hsiang
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Kuo, Bor-Chen
Past studies reported that the main electrogastrography (EEG) dynamic changes related to motion sickness (MS) were occurred in occipital, parietal, and somatosensory brain area, especially in the power increasing of the alpha band (8-13 Hz) and theta band (4-7 Hz) which had positive correlation with the subjective MS level. Depend on these main findings correlated with MS, we attempt to develop an EEG based classification system to automatically classify subject's MS level and find the suitable EEG features via common feature extraction, selection and classifiers technologies in this study. If we can find the regulations and then develop an algorithm to predict MS occurring, it would be a great benefit to construct a safe and comfortable environment for all drivers and passengers when they are cruising in the car, bus, ship or airplane. EEG is one of the best methods for monitoring the brain dynamics induced by motion-sickness because of its high temporal resolution and portability. After collecting the EEG signals and subjective MS level in a realistic driving environment, we first do the data pre-processing part including ICA, component clustering analysis and time-frequency analysis. Then we adopt three common feature extractions and two feature selections (FE/FS) technologies to extract or select the correlated features such as principal component analysis (PCA), linear discriminate analysis (LDA), nonparametric weighted feature extraction (NWFE), forward feature selections (FFS) and backward feature selections (BFS) and feed the feature maps into three classifiers (Gaussian Maximum Likelihood Classifier (ML), k-Nearest-Neighbor Classifier (kNN) and Support Vector Machine (SVM)). Experimental results show that classification performance of all our proposed technologies can be reached almost over 95%. It means it is possible to apply the effective technology combination to predict the subject's MS level in the real life applications. The better combination in this study is using LDA and Gaussian based ML classifier. This advantage can be widely used in machine learning area for developing the prediction algorithms in the future.
APPLYING OPTIMAL ALGORITHM TO DATA-DEPENDENT KERNEL FOR HYPERSPECTRAL IMAGE CLASSIFICATION
(2010)
Chen, I-Ling
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Li, Cheng-Hsuan
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Kuo, Bor-Chen
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Huang, Hsiao-Yun
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電控工程研究所
In the kernel methods, it is very important to choose a proper kernel function to avoid overlapping data. Based this fact, in this paper we mainly utilize a unified kernel optimization framework on the hyperspectral image classification to augment the margin between different classes, and under the kernel optimization framework, to employ the Fisher discriminant criteria formulated in a pairwise manner as the objective functions to optimize the kernel function in Kernel-based nonparametric weighted feature extraction. The experimental results display the superiority of the optimizing kernel function over the RBF kernel function with 5-fold cross-validation method, especially, in the small sample size problem.
Classification of driver's cognitive responses using nonparametric single-trial EEG analysis
(2007)
Lin, Chin-Teng
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Ko, Li-Wei
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Lin, Ken-Li
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Kuo, Bor-Chen
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Liang, Sheng-Fu
Accidents caused by errors and failures in human performance among traffic fatalities have a high rate causing death and become an important issue in public security. The key problem causing these car accidents is mainly because that the drivers failed to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for ongoing assessment of driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-fight experiments in a virtual-reality (VR) dynamic driving environment. Three different feature extraction methods including Nonparametric Weighted Feature Extraction (NWFE), Principlal Component Analysis (PCA), Discriminant Analysis Feature Extraction (DAFE) are applied to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data can be classified with fewer features and their classification results are compared by utilizing three different classifiers including Gaussian classifier (GC), k Nearest neighbor classification (KNNC), and Naive Bayes Classifier (NBC). Experimental results show that the successful rate of Nonparametric Weighted Feature Extraction combined with Gaussian classifier is higher more than 10% compared with other combinations. It also demonstrates the feasibility of detecting and analyzing single-trail ERP signals that represent operators' cognitive states and responses to task events.
COMBINING ENSEMBLE TECHNIQUE OF SUPPORT VECTOR MACHINES WITH THE OPTIMAL KERNEL METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION
(2011)
Kuo, Bor-Chen
;
Chen, I-Ling
;
Li, Cheng-Hsuan
;
Hung, Chih-Cheng
;
電控工程研究所
In remote sensing researches, the curse of dimensionality is one greatly difficult classification problem. Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), can alleviate small sample size and high dimensionality concern and obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. A dynamic subspace method (DSM) was proposed for constructing component classifiers with adaptive subspaces to adjust the shortcomings of RSM based on resubstitution accuracy by applying each classifier. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. The objective of this research is to develop a novel ensemble technique based on support vector machines (SVMs) via the optimal kernel method, and propose a novel subspace selection mechanism, named the kernel-based dynamic subspace method (KDSM), to improve DSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Experimental results show a sound performance of classification on the famous hyperspectral images, Washington DC Mall.
Driver's Cognitive State Classification toward Brain Computer Interface via using a Generalized and Supervised Technology
(2010)
Chuang, Chun-Hsiang
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Lai, Pei-Chen
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Ko, Li-Wei
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Kuo, Bor-Chen
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Lin, Chin-Teng
Growing numbers of traffic accidents had become a serious social safety problem in recent years. The main factor of the high fatalities was the obvious decline of the driver's cognitive state in their perception, recognition and vehicle control abilities while being sleepy. The key to avoid the terrible consequents is to build a detecting system for ongoing assessment of driver's cognitive state. A quickly growing research, brain-computer interface (BCI), offers a solution offering great assistance to those who require alternative communicatory and control mechanisms. In this study, we propose an alertness/drowsiness classification system based on investigating electroencephalographic (EEG) brain dynamics in lane-keeping driving experiments in a virtual reality (VR) driving environment with a motion platform. The core of the classification system is composed of dimension reduction technique and classifier learning algorithm. In order to find the suitable method for better describing the data structure, we explore the performances using different feature extraction and feature selection methods with different classifiers. Experiment results show that the accuracy is over 80% in most combinations and even near 90% under Principal Component Analysis (PCA) and Nonparametric Weighted Feature Extraction (NWFE) going with Gaussian Maximum Likelihood classifier (ML) and k-Nearest-Neighbor classifier (kNN), respectively. In addition, this developed classification system can also solve the individual brain dynamic differences caused from different subjects and overcome the subject dependent limitation. The optimized solution with better accuracy performance out of all combinations can be considered to implement in the kernel brain-computer interface.
HYPERSPECTRAL IMAGE CLASSIFICATION USING SPECTRAL AND SPATIAL INFORMATION BASED LINEAR DISCRIMINANT ANALYSIS
(2011)
Li, Cheng-Hsuan
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Chu, Hui-Shan
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Kuo, Bor-Chen
;
Lin, Chin-Teng
;
電控工程研究所
Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, spatial information is acquired by the concept of the Markov random field (MRF), and this spatial information is used to form the membership values of every pixel in the hyperspectral image. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.
Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification
(2009-04-01)
Kuo, Bor-Chen
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Li, Cheng-Hsuan
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Yang, Jinn-Min
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電控工程研究所
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Institute of Electrical and Control Engineering
In recent years, many studies show that kernel methods are computationally efficient, robust, and stable for pattern analysis. Many kernel-based classifiers were designed and applied to classify remote-sensed data, and some results show that kernel-based classifiers have satisfying performances. Many studies about hyperspectral image classification also show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. However, NWFE is still based on linear transformation. In this paper, the kernel method is applied to extend NWFE to kernel-based NWFE (KNWFE). The new KNWFE possesses the advantages of both linear and nonlinear transformation, and the experimental results show that KNWFE outperforms NWFE, decision-boundary feature extraction, independent component analysis, kernel-based principal component analysis, and generalized discriminant analysis.
LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction
(2011-02-01)
Li, Cheng-Hsuan
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Kuo, Bor-Chen
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Lin, Chin-Teng
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電控工程研究所
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腦科學研究中心
Research has shown fuzzy c-means (FCM) clustering to be a powerful tool to partition samples into different categories. However, the objective function of FCM is based only on the sum of distances of samples to their cluster centers, which is equal to the trace of the within-cluster scatter matrix. In this study, we propose a clustering algorithm based on both within-and between-cluster scatter matrices, extended from linear discriminant analysis (LDA), and its application to an unsupervised feature extraction (FE). Our proposed methods comprise between-and within-cluster scatter matrices modified from the between-and within-class scatter matrices of LDA. The scatter matrices of LDA are special cases of our proposed unsupervised scatter matrices. The results of experiments on both synthetic and real data show that the proposed clustering algorithm can generate similar or better clustering results than 11 popular clustering algorithms: K-means, K-medoid, FCM, the Gustafson-Kessel, Gath-Geva, possibilistic c-means (PCM), fuzzy PCM, possibilistic FCM, fuzzy compactness and separation, a fuzzy clustering algorithm based on a fuzzy treatment of finite mixtures of multivariate Student's t distributions algorithms, and a fuzzy mixture of the Student's t factor analyzers model. The results also show that the proposed FE outperforms principal component analysis and independent component analysis.
Nonparametric single-trial EEG feature extraction and classification of driver's cognitive responses
(2008)
Lin, Chin-Teng
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Lin, Ken-Li
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Ko, Li-Wei
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Liang, Sheng-Fu
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Kuo, Bor-Chen
We proposed an electroencephalographic (EEG) signal analysis approach to investigate the driver's cognitive response to traffic-light experiments in a virtual-reality-(VR-) based simulated driving environment. EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), and linear discriminant analysis (LDA), which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data could be classified with fewer features and their classification results were compared by utilizing two different classifiers including k nearest neighbor classification (KNNC) and naive bayes classifier (NBC). Experimental data were collected from 6 subjects and the results show that NWFE+NBC gives the best classification accuracy ranging from 71%similar to 77%, which is over 10%similar to 24% higher than LDA+KNN1. It also demonstrates the feasibility of detecting and analyzing single-trial EEG signals that represent operators' cognitive states and responses to task events. Copyright (C) 2008 Chin-Teng Lin et al.
SUBSPACE SELECTION BASED MULTIPLE CLASSIFIER SYSTEMS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
(2009)
Kuo, Bor-Chen
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Chuang, Chun-Hsiang
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Li, Cheng-Hsuan
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Lin, Chin-Teng
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電控工程研究所
In a typical supervised classification task, the size of training data fundamentally affects the generality of a classifier. Given a finite and fixed size of training data, the classification result may be degraded as the number of features (dimensionality) increase. Many researches have demonstrated that multiple classifier systems (MCS) or so-called ensembles can alleviate small sample size and high dimensionality concern, and obtain more outstanding and robust results than single models. One of the effective approaches for generating an ensemble of diverse base classifiers is the use of different feature subsets such as random subspace method (RSM). The objective of this research is to develop a novel ensemble technique based on cluster algorithms for strengthening RSM. The results of real data experiments show that the proposed method obtains the sound performance especially in the situation of using less number of classifiers.
VALIDITY AND INTERNAL CONSISTENCY RELIABILITY OF A COMPUTERIZED TEST TO ASSESS PRONE EXTENSION IN CHILDREN AGES FOUR TO SIX YEARS
(2010-08-01)
Lin, Chin-Kai
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Wu, Huey-Min
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Kuo, Bor-Chen
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Li, Cheng-Hsaun
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電控工程研究所
The objective of this study was to develop a computerized test of prone extension to measure performance of prone extension in children 4 to 6 years of age. The participants were selected from kindergartens, comprising 132 boys and 107 girls with a mean age of 5 yr. 2 mo. (SD = 6 mo.). Sensitivity and specificity of the computerized test of prone extension were assessed by comparison with the judgments of an expert, an occupational therapist with more than 20 yr. of pediatric experience, as the criterion standard. The computerized test of prone extension identified children with poor outcomes with a sensitivity of 0.83, a specificity of 0.88, and an accuracy of 0.87. The internal reliability index was 0.81. The computerized test of prone extension could be of value in detecting problems of antigravity posture in prone extension and permitting early intervention to correct it.