Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at each boosting step. Good testing performance of abc-boost (implemented as abc...

PURPOSE We have focused on finding a classifier that best discriminates between tumour progression and regression based on multiparametric MR data retrieved from follow-up GBM patients. MATERIALS AND METHODS Multiparametric MR data consisting of conventional and advanced MRI (perfusion, diffusion, and spectroscopy) were acquired from 29 GBM patients treated with adjuvant therapy after surgery...

In this paper, we present a fast method to detect humans from videos captured in surveillance applications. It is based on a cascade of LogitBoost classifiers relying on features mapped from the Riemanian manifold of region covariance matrices computed from input image features. The method was extended in several ways. First, as the mapping process is slow for high dimensional feature space, we...

Management of acute gastrointestinal bleeding necessitates the identification of the source of bleed. The source of bleeding which is clear in patients presenting with hematemesis, is unclear in the absence of it. Logistic regression, decision tree, naïve Bayes, LogitBoost and KNN models were constructed from non endoscopic data of 325 patients admitted via the emergence department (ED) for GIB...

Serodiagnosis of arthropod-borne viruses (arboviruses) at the Division of Vector-Borne Diseases, CDC, employs a combination of individual enzyme-linked immunosorbent assays and microsphere immunoassays (MIAs) to test for IgM and IgG, followed by confirmatory plaque-reduction neutralization tests. Based upon the geographic origin of a sample, it may be tested concurrently for multiple arboviruse...

Due to the recent overwhelming growth rate of large-scale data, the development of faster processing algorithms with optimal performance has become a dire need of the time. In this paper, we propose two novel algorithms, ADABOOST.PL (Parallel ADABOOST) and LOGITBOOST.PL (Parallel LOGITBOOST), that facilitate simultaneous participation of multiple computing nodes to construct a boosted classifie...

The students’ performance prediction (SPP) problem is a challenging that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning systems, other not simple task. Even after collecting data, we might imbalanced missing biased different types strings, numbers, letters. One of the most common ch...