• Information Systems Review
• /
• v.24 no.1
• /
• pp.39-57
• /
• 2022

This paper proposes the GMOPTBoost algorithm to improve the performance of the AdaBoost algorithm for bankruptcy prediction in which class imbalance problem is inherent. AdaBoost algorithm has the advantage of providing a robust learning opportunity for misclassified samples. However, there is a limitation in addressing class imbalance problem because the concept of arithmetic mean accuracy is embedded in AdaBoost algorithm. GMOPTBoost can optimize the geometric mean accuracy and effectively solve the category imbalance problem by applying Gaussian gradient descent. The samples are constructed according to the following two phases. First, five class imbalance datasets are constructed to verify the effect of the class imbalance problem on the performance of the prediction model and the performance improvement effect of GMOPTBoost. Second, class balanced data are constituted through data sampling techniques to verify the performance improvement effect of GMOPTBoost. The main results of 30 times of cross-validation analyzes are as follows. First, the class imbalance problem degrades the performance of ensembles. Second, GMOPTBoost contributes to performance improvements of AdaBoost ensembles trained on imbalanced datasets. Third, Data sampling techniques have a positive impact on performance improvement. Finally, GMOPTBoost contributes to significant performance improvement of AdaBoost ensembles trained on balanced datasets.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.5
• /
• pp.843-848
• /
• 2008

Most of face detection technique uses information from the face of the movement. The traditional face detection method is to use difference picture method ate used to detect movement. However, most do not consider this mathematical approach using real-time or real-time implementation of the algorithm is complicated, not easy. This paper, the first to detect real-time facial image is converted YCbCr and RGB video input. Next, you convert the difference between video images of two adjacent to obtain and then to conduct Glassfire Labeling. Labeling value compared to the threshold behavior Area recognizes and converts video extracts. Actions to convert video to conduct face detection, and detection of facial characteristics required for the extraction and use of AdaBoost algorithm.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.3E
• /
• pp.124-132
• /
• 2003

We propose a new method for speech/non-speech classifiers based on concepts of the adaptive boosting (AdaBoost) algorithm in order to detect speech for robust speech recognition. The method uses a combination of simple base classifiers through the AdaBoost algorithm and a set of optimized speech features combined with spectral subtraction. The key benefits of this method are the simple implementation, low computational complexity and the avoidance of the over-fitting problem. We checked the validity of the method by comparing its performance with the speech/non-speech classifier used in a standard voice activity detector. For speech recognition purpose, additional performance improvements were achieved by the adoption of new features including speech band energies and MFCC-based spectral distortion. For the same false alarm rate, the method reduced 20-50% of miss errors.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.1 no.3
• /
• pp.83-89
• /
• 2008

This paper introduces a visual object classification algorithm based on statistical information. Objects are characterized through the Histogram of Oriented Gradients (HOG) method and classification is performed using Multiclass AdaBoost. Salient features of an object's appearance are detected by HOG blocks Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification a multiclass AdaBoostSVM algorithm is applied. The proposed method has a high speed processing and classification rate. Results of the evaluation based on example of hand gesture recognition are presented.

• ETRI Journal
• /
• v.32 no.5
• /
• pp.761-765
• /
• 2010

In this paper, we introduce an improved adaptive boosting (AdaBoost) classifier and its application, a disguised-face discriminator that discriminates between bare and disguised faces. The proposed classifier is based on an AdaBoost learning algorithm and regression technique. In the process, the lookup table of AdaBoost learning is utilized. The proposed method is verified on the captured images under several real environments. Experimental results and analysis show the proposed method has a higher and faster performance than other well-known methods.

• Journal of Power Electronics
• /
• v.19 no.2
• /
• pp.443-453
• /
• 2019

As the core component of transmission systems, converters are very prone to failure. To improve the accuracy of fault diagnosis for wind power converters, a fault feature extraction method combined with a wavelet transform and compressed sensing theory is proposed. In addition, an improved AdaBoost-SVM is used to diagnose wind power converters. The three-phase output current signal is selected as the research object and is processed by the wavelet transform to reduce the signal noise. The wavelet approximation coefficients are dimensionality reduced to obtain measurement signals based on the theory of compressive sensing. A sparse vector is obtained by the orthogonal matching pursuit algorithm, and then the fault feature vector is extracted. The fault feature vectors are input to the improved AdaBoost-SVM classifier to realize fault diagnosis. Simulation results show that this method can effectively realize the fault diagnosis of the power transistors in converters and improve the precision of fault diagnosis.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.743-744
• /
• 2008

By using DirectX technology, an improved Visual Monitoring System implemented in this paper. The proposed Visual Monitoring System is developed based on the S3C2440 processor. The Windows CE 5.0 is adopted as an operating system, and Visual Monitoring System transfer image 15 frame per second using UDP/IP and by using AdaBoost Algorithm, detect face region and save face image.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.2
• /
• pp.129-134
• /
• 2010

Diseases such as cardiovascular illnesses, according to the National Statistical Office opened reveals that 600-800 people were killed, blood pressure, arteriosclerosis, heart disease, stroke, etc. will be a flow of blood disorders that occur in cardiovascular illnesses today are fulfilling the Master / Slave samangryulin disease appears high. Died of cardiovascular disease also told them the correct first aid survival when patients are accounted for approximately 40% of emergency rapid response is required. Therefore, this paper, the weak classifier in the AdaBoost algorithm to generate a strong classifier by combining effects throughout the analysis to measure the ECG, and cardiovascular disease that occurred to you as soon as the emergency management system that can deliver on the proposed Desk was. The electrocardiogram data measured by the ZigBee-based sensors, communication devices and emergency transport for emergency alarms in the determination and monitoring of the management desk by providing health services to enable the delivery was fast.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.11
• /
• pp.193-201
• /
• 2014

This paper suggests a facial expression recognition system using face detection, face alignment, facial unit extraction, and training and testing algorithms based on AdaBoost classifiers. First, we find face region by a face detector. From the results, face alignment algorithm extracts feature points. The facial units are from a subset of action units generated by combining the obtained feature points. The facial units are generally more effective for smaller-sized databases, and are able to represent the facial expressions more efficiently and reduce the computation time, and hence can be applied to real-time scenarios. Experimental results in real scenarios showed that the proposed system has an excellent performance over 90% recognition rates.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.11
• /
• pp.1074-1081
• /
• 2007

This paper presents a method for real-time face detection and tracking which combined Adaboost and Camshift algorithm. Adaboost algorithm is a method which selects an important feature called weak classifier among many possible image features by tuning weight of each feature from learning candidates. Even though excellent performance extracting the object, computing time of the algorithm is very high with window size of multi-scale to search image region. So direct application of the method is not easy for real-time tasks such as multi-task OS, robot, and mobile environment. But CAMshift method is an improvement of Mean-shift algorithm for the video streaming environment and track the interesting object at high speed based on hue value of the target region. The detection efficiency of the method is not good for environment of dynamic illumination. We propose a combined method of Adaboost and CAMshift to improve the computing speed with good face detection performance. The method was proved for real image sequences including single and more faces.