The k-Nearest Neighbor (kNN) classification approach is conceptually simple – yet widely applied since it often performs well in practical applications. However, using a global constant k does not always provide an optimal solution, e. g., for datasets with an irregular density distribution of data points. This paper proposes an adaptive kNN classifier where k is chosen dynamically for each ins...

k Nearest Neighbors (kNN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based kNN algorithm which can effectively handle both Gaussian d...

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...

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...

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...

The discovery of piezoelectricity in natural bone has attracted extensive research in emulating biological electricity for various tissue regeneration. Here, we carried out experiments to build biocompatible potassium sodium niobate (KNN) ceramics. Then, influence substrate surface charges on bovine serum albumin (BSA) protein adsorption and cell proliferation on KNN ceramics surfaces was inves...