• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.603-614
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• 2022

The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers' face shape, demonstrating its utility in the top-k classification problem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.348-358
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• 2022

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.521-524
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• 2003

In this paper, we propose a design process of 'personalized' classification with soft computing techniques. Based on human's thinking way, a construction methodology for personalized classifier is mentioned. Here, two fuzzy similarity measures and ensemble of classifiers are effectively used. As one of the possible applications, facial expression recognition problem is discussed. The numerical result shows that the proposed method is very useful for on-line learning, reusability of previous knowledge and so on.

• Archives of Reconstructive Microsurgery
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• v.18 no.1
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• pp.16-22
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• 2009

The purpose of this study was to present the clinical result of anterolateral thigh free flap for pretibial soft tissue lesion after chronic tibia osteomyelitis. From December 2006 to September 2008, Five patients were included in our study. 4 of 5 were superficial or localized types of chronic tibia osteomyelitis, based on the classification of Cierny and Mader. Average age at the surgery was 45 years, three were males and two were females. All had a history of chronic tibia osteomyelitis and subsequent pretbial soft tissue lesions coming from previous operations or pus drainage. Pretibial soft tissue defects included small ulcers, fibrotic, bruisable soft tissue and small bony exposures, but not large-sized bony exposures nor active pus discharge. After complete debridement of large sized pretibial soft tissue lesions and decortication of anterior tibial cortical dead bone, anterolateral thigh free flap was applied to cover remained large pretibial soft tissue defect and to prevent the recurrence of infection. All flaps survived and provided satisfactory coverage of soft tissue defect on pretibial region for 16 months' mean follow up period. No patients has had recurrence of osteomyelitis. Anterolateral thigh free flap could be recommend for large sized pretibial soft tissue defect of supreficial or localized types of chronic tibia osteomyelitis after through debridement.

• Geotechnical Engineering
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• v.14 no.6
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• pp.45-56
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• 1998

In order to select a proper ground improvement technology and to assess the quality and rate of improvement in the soft deposits. it is essential to characterize in-situ properties of the soft marine clay layer that may have many thin silt or sand seams. In this paper, both piezocone and flat dilatometer tests were performed to characterize in situ properties of a marine clay. Both tests provided quite similar site classifications, and in both tests the penetration pore water pressure was the better indicator for the classification of marine clay layer, especially in which sand or silt seams are frequently interbedded. Undrained strengths determined by both the cone tip resistance and the excess pore water pressure measured from piezocone were very similar in clayey soil layers. And the untrained strength determined by dilatometer had an approximately average value of undiained strengths obtained from piezocone. In addition, the theoretical time factor that can consider pore pressure dissipation effect during cone penetration may provide a reliable estimation of the coefficient of consolidation, especially for a coastal site which includes many silt or sand fractions or seams.

• Tunnel and Underground Space
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• v.19 no.5
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• pp.407-420
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• 2009

In this study geostatistical technique using indicator kriging was performed to evaluate the optimal rock mass classification by integrating the various geophysical information such as borehole data and geophysical data. To get the optimal kriging result, it is necessary to devise the suitable technique to integrate the hard (borehole) and soft (geophysical) data effectively. Also, the model parameters of the variogram must be determined as a priori procedure. Iterative non-linear inversion method was implemented to determine the model parameters of theoretical variogram. To verify the algorithm, behaviour of object function and precision of convergence were investigated, revealing that gradient of the range is extremely small. This algorithm for the field data was applied to a mountainous area planned for a large-scale tunneling construction. As for a soft data, resistivity information from AMT survey is incorporated with RMR information from borehole data, a sort of hard data. Finally, RMR profiles were constructed and attempted to be interpreted at the tunnel elevation and the upper 1D level.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.635-644
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• 2019

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.658-663
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• 2010

Recently the most of deep foundation were socketed into weathered rock or soft rock to carry large foundation loads. The end bearing behavior of piles socketed in rock is generally dependent on the rock mass conditions with discontinuities and rock strength. Therefore, it is very important that the estimating rock classification with relation of TCR, RQD and unpredicted rock condition. In this study, the construction failure example of drilled shaft due to mistaking to estimate the rock classification on penetration were analyzed in site, so we hope to discuss problems of determining the rock socketed length of drilled shaft on construction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.