Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situations. K-nearest neighbors algorithm (K-NN) have been used in CBR systems to define new cases status according to characteristics of past nearest cases. We proposed a new hybrid approach combining logistic regression (LR) with K-NN to optimize CBR classification. First, we analyzed the knowledge dat...

Classification of objects is an important area in a variety of fields and applications. Many different methods are available to make a decision in those cases. The knearest neighbor rule (k-NN) is a well-known nonparametric decision procedure. Classification rules based on the k-NN have already been proposed and applied in diverse substantive areas. The editing k-NN proposed by Wilson would be ...

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...

We propose a neural network (NN)-based adaptive modulation and coding (AMC) for link adaptation in MIMO-OFDM systems. The AMC optimizes the best modulation and coding scheme (MCS) under a packet error rate (PER) constraint. In our approach, a NN with a multilayer perceptron (MLP) structure is applied for the AMC and its performance is compared with the k-nearest neighbor (k-NN) algorithm under ...

Developing a stable early warning system (EWS) model that is capable to give an accurate prediction is a challenging task. This paper introduces k-nearest neighbour (k-NN) method which never been applied in predicting currency crisis before with the aim of increasing the prediction accuracy. The proposed k-NN performance depends on the choice of a distance that is used where in our analysis; we...

In this work a quantum sorting algorithm with adaptable requirements of memory and circuit depth is introduced, used to develop new version the classical machine learning known as k-nearest neighbors (k-NN). Both efficiency performance k-NN are compared those another proposed by Schuld et al. Results show that both algorithms similar each other superior algorithm. On hand, one k-NN.

Facial Expression is a key component in evaluating a person's feelings, intentions and characteristics. Facial Expression is an important part of human-computer interaction and has the potential to play an equal important role in humancomputer interaction. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and KNearest Neighbor (K-NN) applying for fac...

Many sophisticated classification algorithms have been proposed. However, there is no clear methodology of comparing the results among different methods. According to our experiments on the popular datasets, k-NN with properly tuned parameters performs on average best. Tuning the parametres include the proper k, proper distance measure and proper weighing functions. k-NN has a zero training tim...

To improve the localization accuracy in multipath environments, this paper presents an effective localization approach with the utilization of reference tags. In this approach, an improved k-nearest neighbor (k-NN) algorithm is proposed based on radio-frequency (RF) phases. The traditional k-NN algorithm only focuses on the weighting factors of the coordinates of the selected reference tags, wh...