AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine SVM , neural networks NN , naı̈ve Bayes, and k-nearest neighbor kNN . This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple tw...

A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) ...

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-...

Machine learning algorithms have been widely used in public health for predicting or diagnosing epidemiological chronic diseases, such as diabetes mellitus, which is classified an epi-demic due to its high rates of global prevalence. techniques are useful the processes description, prediction, and evaluation various including diabetes. This study investigates ability different classification me...

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values river. However, due huge data collection on pollution with uncertain water parameter values, need a different approach classify quality. One supervised algorithms known as K-Nearest Neighbors (KNN) seems give new for where each points are classified according k number or closest neighb...

In data mining applications, one of the useful algorithms for classification is the kNN algorithm. The kNN search has a wide usage in many research and industrial domains like 3-dimensional object rendering, content-based image retrieval, statistics, biology (gene classification), etc. In spite of some improvements in the last decades, the computation time required by the kNN search remains the...

KNN algorithm is a simple, effective, non-parametric classification, and has been widely used in text classification, pattern recognition, image and spatial classification. Research on improvements about KNN algorithm has broad application prospects and important scientific significance. Based on analysis about classic KNN and its improved algorithms, we find its over-reliance on the choice of ...

The k Nearest Neighbor (kNN) join operation associates each data object in one data set with its k nearest neighbors from the same or a different data set. The kNN join on high-dimensional data (high-dimensional kNN join) is an especially expensive operation. Existing high-dimensional kNN join algorithms were designed for static data sets and therefore cannot handle updates efficiently. In this...