Multi-class AdaBoost Algorithm Research Articles

Islanding detection in DC ring microgrid using improved complete ensemble empirical mode decomposition with adaptive noise and multi-class AdaBoost algorithm

Islanding detection in DC ring microgrid using improved complete ensemble empirical mode decomposition with adaptive noise and multi-class AdaBoost algorithm

Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm

DC microgrids with energy storage systems based on photovoltaic (PV) and wind energy are gaining popularity as a means to offer users with reliable supply in either a stand-alone or grid connected mode. However, because DC and AC side faults have similar current–voltage profiles, developing a viable safety strategy for the proposed integrated DC microgrid is difficult. Traditional protection techniques based on pre-defined thresholds are unable to discriminate between DC and AC side faults, and so fail to offer independent control actions in both circumstances. In this context, new morphological operators with improved AdaBoost algorithm is proposed for detecting and classifying the AC and DC side faults in the proposed DC microgrid. To explore this, current signals are captured at the DC bus of the proposed integrated DC microgrid. The captured signals comprise background noise which is eliminated by dilation erosion difference operator (DEDO) and opening closing difference (OCDO) operators. The two operators work together to meet the accurate fault detection to avoid nuisance tripping by multiscale operation of structuring element (SE). For effective outcomes the multiple scales are optimized by sparse kurtosis (SK) index. The optimized scales are passes through target features to retrieve the data. The acquired data is sent into the multi-class AdaBoost approach, which recognizes faults by modifying the distribution of data and iteratively adjusting the weight of each instance. The proposed system's efficacy is tested using the MATLAB/Simulink platform under various operating situations such as load variation, irradiation and fault resistance changes. The proposed algorithm's superiority is demonstrated by comparing it to existing approaches using confusion matrix (CM) parameters.

Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm

Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

AdaBoosted Extra Trees Classifier for Object-Based Multispectral Image Classification of Urban Fringe Area

In the past decade, it is proven that satellite image classification using an object-based technique is better than the standard pixel-based technique. With the increasing need for classifying multispectral satellite images for urban planning, the accuracy of the classification becomes a significant performance parameter. Object-based classification (OBC) is a technique in which group of pixels having similar spectral properties, called objects, are generated using image segmentation and then these objects are classified based on their attributes. In this paper, the combination of a multiclass AdaBoost algorithm with extra trees classifier (ETC) is proposed with higher prediction accuracy for the OBC of the urban fringe area. The performance of the AdaBoost algorithm is found to be better in terms of classification accuracy than benchmarked SVM and RF classifiers for OBC. These classification methods were applied to IRS-R2 LISS IV data. The AdaBoosted extra trees classifier (ABETC) has demonstrated the highest accuracy with overall accuracy (OA) of 88.47% and a kappa coefficient of 0.85. The computational time of the ABETC is found to be much smaller than the RF algorithm. In detail, the sensitivity of the classifiers was investigated using stratified random sampling with various sample sizes.

Homogeneity patch search method for voting-based efficient vehicle color classification using front-of-vehicle image

Color classification plays a significant role in tracking crime vehicles. Color features can be used to classify vehicle color using a front-of-vehicle image acquired from CCTV. In this paper, we define a new color homogeneity patch and propose search method that focuses on improving the discernment of features by avoiding another color distribution, not a vehicle color, which is included in the vehicle color features. In addition, we could effectively improve this method by using a voting strategy. A region of interest (ROI) that includes a bonnet is detected using predefined information about the given car and the search method is applied to ROI for selecting color homogeneity patches. The proposed approach extracts an HSV histogram for each patch, and adopts the multi-class Adaboost algorithm to classify the color of each patch. We integrate the proposed method with the voting strategy to determine the color of the vehicle. To validate the feasibility of our approach, we compare the results with a sliding window without consideration of homogeneity. Results show that the search method for homogeneity patches can be efficiently applied to recognize vehicle color.

Emotion recognition based on facial components

Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human–computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from these expressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques.

Facial Emotion Recognition Based on Eye and Mouth Regions

Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human-computer intelligent interaction nowadays. The eye and the mouth regions are most essential components for facial emotion recognition. Most of the existing approaches have not utilized the eye and the mouth regions for high recognition rate. This paper proposes an approach to overcome this limitation using the eye and the mouth region-based emotion recognition using reinforced local binary patterns (LBP). The local features are extracted in each frame by using Gabor wavelet with selected scale and orientations. This feature is passed on to the ensemble classifier for detecting the location of the face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using ensemble classifier. The eye and the mouth features are extracted using reinforced LBP. Multi-class Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and FERA 2011 databases, and they exhibit significant performance improvement owing to their novel features when compared to the existing techniques.

A robust multi-class AdaBoost algorithm for mislabeled noisy data

AdaBoost has been theoretically and empirically proved to be a very successful ensemble learning algorithm, which iteratively generates a set of diverse weak learners and combines their outputs using the weighted majority voting rule as the final decision. However, in some cases, AdaBoost leads to overfitting especially for mislabeled noisy training examples, resulting in both its degraded generalization performance and non-robustness. Recently, a representative approach named noise-detection based AdaBoost (ND_AdaBoost) has been proposed to improve the robustness of AdaBoost in the two-class classification scenario, however, in the multi-class scenario, this approach can hardly achieve satisfactory performance due to the following three reasons. (1) If we decompose a multi-class classification problem using such strategies as one-versus-all or one-versus-one, the obtained two-class problems usually have imbalanced training sets, which negatively influences the performance of ND_AdaBoost. (2) If we directly apply ND_AdaBoost to the multi-class classification scenario, its two-class loss function is no longer applicable and its accuracy requirement for the (weak) base classifiers, i.e., greater than 0.5, is too strong to be almost satisfied. (3) ND_AdaBoost still has the tendency of overfitting as it increases the weights of correctly classified noisy examples, which could make it focus on learning these noisy examples in the subsequent iterations. To solve the dilemma, in this paper, we propose a robust multi-class AdaBoost algorithm (Rob_MulAda) whose key ingredients consist in a noise-detection based multi-class loss function and a new weight updating scheme. Experimental study indicates that our newly-proposed weight updating scheme is indeed more robust to mislabeled noises than that of ND_AdaBoost in both two-class and multi-class scenarios. In addition, through the comparison experiments, we also verify the effectiveness of Rob_MulAda and provide a suggestion in choosing the most appropriate noise-alleviating approach according to the concrete noise level in practical applications.

Hyperspectral Image Classification With Limited Labeled Training Samples Using Enhanced Ensemble Learning and Conditional Random Fields

Classification of hyperspectral imagery using few labeled samples is a challenging problem, considering the high dimensionality of hyperspectral imagery. Classifiers trained on limited samples with abundant spectral bands tend to overfit, leading to weak generalization capability. To address this problem, we have developed an enhanced ensemble method called multiclass boosted rotation forest (MBRF), which combines the rotation forest algorithm and a multiclass AdaBoost algorithm. The benefit of this combination can be explained by bias-variance analysis, especially in the situation of inadequate training samples and high dimensionality. Furthermore, MBRF innately produces posterior probabilities inherited from AdaBoost, which are served as the unary potentials of the conditional random field (CRF) model to incorporate spatial context information. Experimental results show that the classification accuracy by MBRF as well as its integration with CRF consistently outperforms the other referenced state-of-the-art classification methods when limited labeled samples are available for training.

Application of Multi-Class AdaBoost Algorithm to Terrain Classification of Satellite Images

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

Human Activity Recognition Using Smart-Phone Sensors

Activity recognition is a challenging problem for context-aware systems and applications. Many studies in this field has mainly adopted techniques based on supervised or semi-supervised learning algorithms to recognize activities by movement patterns gathered through sensors, but these existing systems suffer from complex issues for feature representations of sensor data and multi-sensors integration. In this paper, we propose a novel feature learning method for activity recognition based on entropy and construct an activity recognition model with multi-class AdaBoost algorithm. Experiments on sensor data from a real dataset demonstrate the significant potential of our method to extract features for activity recognition. The experimental results also show recognition model based on multi-class AdaBoost is effective. The average precision and recall for six activities are 95.9% and 95.9%, respectively, which are higher than results obtained by using other methods such as Support Vector Machine (SVM) or K-Nearest Neighbor (KNN).

Discrimination of breast tumors in ultrasonic images by classifier ensemble trained with AdaBoost

AbstractIn this paper, we propose a novel method for accurate automated discrimination of breast tumors (carcinoma, fibroadenoma, and cyst). We defined 199 features related to diagnostic observations noticed when a doctor judges breast tumors, such as internal echo, shape, and boundary echo. These features included novel features based on a parameter of log‐compressed K distribution, which reflect physical characteristics of ultrasonic B‐mode imaging. Furthermore, we propose a discrimination method of breast tumors by using an ensemble classifier based on the multiclass AdaBoost algorithm with effective features selection. Verification by analyzing 200 carcinomas, 30 fibroadenomas, and 30 cysts showed the usefulness of the newly defined features and the effectiveness of the discrimination by using an ensemble classifier trained by AdaBoost. © 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94(9): 18–29, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10356

Human action recognition using boosted EigenActions

This paper proposes a boosting EigenActions algorithm for human action recognition. A spatio-temporal Information Saliency Map (ISM) is calculated from a video sequence by estimating pixel density function. A continuous human action is segmented into a set of primitive periodic motion cycles from information saliency curve. Each cycle of motion is represented by a Salient Action Unit (SAU), which is used to determine the EigenAction using principle component analysis. A human action classifier is developed using multi-class Adaboost algorithm with Bayesian hypothesis as the weak classifier. Given a human action video sequence, the proposed method effectively locates the SAUs in the video, and recognizes the human actions by categorizing the SAUs. Two publicly available human action databases, namely KTH and Weizmann, are selected for evaluation. The average recognition accuracy are 81.5% and 98.3% for KTH and Weizmann databases, respectively. Comparative results with two recent methods and robustness test results are also reported.

AdaBoostによる識別器学習に基づく医用超音波画像における乳腺腫瘤の自動鑑別

In this paper, we propose a novel method for accurate automated discrimination of breast tumors (carcinoma, fibroadenoma, and cyst). We defined 199 features related to diagnostic observations noticed when a doctor judges breast tumors, such as internal echo, shape, and boundary echo. These features included novel features based on a parameter of log-compressed K distribution, which reflect physical characteristics of ultrasonic B-mode imaging. Furthermore, we propose a discrimination method of breast tumors by using an ensemble classifier based on the multiclass AdaBoost algorithm with effective features selection. Verification by analyzing 200 carcinomas, 30 fibroadenomas, and 30 cysts showed the usefulness of the newly defined features and the effectiveness of the discrimination by using an ensemble classifier trained by AdaBoost. © 2011 Wiley Periodicals, Inc. Electron Comm Jpn, 94(9): 18–29, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10356