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.

Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost

The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO2) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO2 signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens’s kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea–hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO2 was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO2 enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.

Multiclass AdaBoost ELM and Its Application in LBP Based Face Recognition

Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation; it can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). As verified by the simulation results, ELM tends to have better scalability and can achieve much better generalization performance and much faster learning speed compared with traditional SVM. In this paper, we introduce a multiclass AdaBoost based ELM ensemble method. In our approach, the ELM algorithm is selected as the basic ensemble predictor due to its rapid speed and good performance. Compared with the existing boosting ELM algorithm, our algorithm can be directly used in multiclass classification problem. We also carried out comparable experiments with face recognition datasets. The experimental results show that the proposed algorithm can not only make the predicting result more stable, but also achieve better generalization performance.