One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...

In multilabel classification each example is represented with features and associated with multiple labels. Multilabel classification aims to predict set of labels for unseen instances. Researchers have developed multilabel classification using both the problem transformation approach and algorithm adaptation approach. An algorithm called MLkNN that follows algorithm adaptation approach has bee...

PURPOSE To assess the effectiveness of two automated magnetic resonance imaging (MRI) segmentation methods in determining the gross tumor volume (GTV) of brain tumors for use in radiation therapy treatment planning. METHODS AND MATERIALS Two automated MRI tumor segmentation methods (supervised k-nearest neighbors [kNN] and automatic knowledge-guided [KG]) were evaluated for their potential as...

The k nearest neighbor (kNN) rule is one of the most used techniques in data mining and pattern recognition due to its simplicity and low identification error. However, the computational effort it requires is directly related to the dataset sizes, hence delivering a poor performance on large datasets. ::: The :::: use :: of :::::::: graphics processing units (GPU) ::: has :::::::: improved ::::...

In this study, we fabricated piezoelectric energy harvesters composed of lead-free (K,Na)NbO3 (KNN) thin films and compared the power generation performance with PZT-thin film energy harvesters. Both of the piezoelectric thin films were deposited on Pt/Ti/Si cantilevers by rf-sputtering. The KNN and PZT thin films had perovskite structure, and showed the relative dielectric constants of 744 and...

A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature s...

The paper describes a new method based on the information-gap theory which enables an evaluation of worst case error predictions of the kNN method in the presence of a specified level of uncertainty in the data. There are presented concepts of a robustness and an opportunity of the kNN model and calculations of these concepts were performed for a simple 1-D data set and next, for a more complic...