This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications...

KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to l...

electroencephalogram (eeg) is one of the useful biological signals to distinguish different brain diseases and mental states. in recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from eeg signals. in this research, we introduce an emot...

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-a...

The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This chapter studies a new estimation for missing (or incomplete) data that i...

Lead-free (K0.5Na0.5)NbO3-LiNbO3 (KNN-LN) and (K0.5Na0.5)NbO3-LiTaO3 (KNN-LT) ferroelectric single crystals, with the dimensions of 11  11  5 mm and 5  5  3 mm, were grown successfully using the top-seeded solution growth (TSSG) method, respectively. The crystal structures were analyzed by means of X-ray diffraction, showing orthorhombic symmetry for KNN-LN single crystals and coexistence o...

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...

DOI reference number: 10.18293/SEKE2015-153 Abstract—Recommendation systems are software tools and techniques that provide customized content to users. The collaborative filtering is one of the most prominent approaches in the recommendation area. Among the collaborative algorithms, one of the most popular is the k-Nearest Neighbors (kNN) which is an instance-based learning method. The kNN gene...

The k-nearest neighbors (knn) is a simple but effective method of classification. In this paper we present an extended version of this technique for chemical compounds used in High Throughput Screening, where the distances of the nearest neighbors can be taken into account. Our algorithm uses kernel weight functions as guidance for the process of defining activity in screening data. Proposed ke...