We present a modification of the weighted K-nearest neighbours imputation method (KNNimpute) for missing values (MVs) estimation in microarray data based on the reuse of estimated data. The method was called iterative KNN imputation (IKNNimpute) as the estimation is performed iteratively using the recently estimated values. The estimation efficiency of IKNNimpute was assessed under different co...

K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both applet and MATLAB program with real life benchmark data to analyze the performan...

The Optimally Pruned Extreme Learning Machine (OPELM) and Optimally Pruned K-Nearest Neighbors (OP-KNN) algorithms use the a similar methodology based on random initialization (OP-ELM) or KNN initialization (OP-KNN) of a Feedforward Neural Network followed by ranking of the neurons; ranking is used to determine the best combination to retain. This is achieved by Leave-One-Out (LOO) crossvalidat...

This paper analyzes the performance of different classification methods for online activity recognition on smart phones using the built-in accelerometers. First, we evaluate the performance of activity recognition using the Naïve Bayes classifier and next we utilize an improvement of Minimum Distance and K-Nearest Neighbor (KNN) classification algorithms, called Clustered KNN. For the purpose o...

The random assignment problem asks for the minimum-cost perfect matching in the complete n × n bipartite graph Knn with i.i.d. edge weights, say uniform on [0, 1]. In a remarkable work by Aldous (2001), the optimal cost was shown to converge to ζ(2) as n → ∞, as conjectured by Mézard and Parisi (1987) through the so-called cavity method. The latter also suggested a non-rigorous decentralized st...

Microarrays allow to determine expressed genes in a given cell type, at a given time and under particular experimental conditions. These experiments are performed on a huge scale and produce numerous data. Methods like Hierarchical Clustering (HC) [1] or Self Organizing Map [2] are often used to identify co-expressed genes. The data often contain several missing values (MV) due to experimental ...

Speech signal is a rich source of information and convey more than spoken words, and can be divided into two main groups: linguistic and nonlinguistic. The linguistic aspects of speech include the properties of the speech signal and word sequence and deal with what is being said. The nonlinguistic properties of speech have more to do with talker attributes such as age, gender, dialect, and emot...

This paper addresses the adaptive autocorrelation approach for the fingerprinting-based distance dependent positioning algorithms (DDPAs) in wireless local area network (WLAN) indoor environment. As far as we know, although the DDPAs, like nearest neighbor (NN), K nearest neighbors (KNN) and weighted KNN (WKNN) algorithms, have been widely utilized for the indoor and outdoor location based serv...

Classification of spatial data has become important due to the fact that there are huge volumes of spatial data now available holding a wealth of valuable information. In this paper we consider the classification of spatial data streams, where the training dataset changes often. New training data arrive continuously and are added to the training set. For these types of data streams, building a ...