The classification of human motion is an important aspect of monitoring pedestrian traffic. This requires the development of advanced surveillance and monitoring systems. Methods to achieve this have been proposed using micro-Doppler radars. However, reliable long-term data and/or complicated procedures are needed to classify motion accurately with these conventional methods because their accur...

In many application areas of machine learning, prior knowledge concerning the monotonicity of relations between the response variable and predictor variables is readily available. Monotonicity may also be an important model requirement with a view toward explaining and justifying decisions, such as acceptance/rejection decisions. We propose a modified nearest neighbour algorithm for the constru...

Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First,...

Ensemble of classifiers is one of the most researched methods in pattern classification in recency. It’s a well-known fact that multiple phases for evaluation provides more accuracy. In this paper we proposed a multistage classifier approach where we are applying three supervised classifiers for the classification in pattern recognition. Three Classifiers are Multilayer Perceptron (MLP), K-Near...

This paper is concerned with a local asym-metric weighting scheme for the nearest neighbor classiication algorithm and a learning procedure, based on reinforcement, for computing the weights. Theoretical results show that this context dependent metric can learn exactly certain classes of concepts storing fewer examples that those required by the Euclidean metric. Moreover, computer experiments ...

As practical pattern classification tasks are often very-large scale and serious imbalance such as patent classification, using traditional pattern classification techniques in a plain way to deal with these tasks has shown inefficient and ineffective. In this paper, a supervised clustering algorithm based on min-max modular network with Gaussian-zero-crossing function is adopted to prune train...

Image annotation is a method for representing an image with a suitable keyword closer to its semantic concept. Automatically assigning relevant text keywords to image is an important problem. Many algorithms and combination of different features have been proposed in the past and achieved good performance. Efforts have focused upon many other fields and some predefined set of features in the ar...

This paper presents the nearest neighbor value (NNV) algorithm for high resolution (H.R.) image interpolation. The difference between the proposed algorithm and conventional nearest neighbor algorithm is that the concept applied, to estimate the missing pixel value, is guided by the nearest value rather than the distance. In other words, the proposed concept selects one pixel, among four direct...

In recent years, the increasing interest in fuzzy rough set theory has allowed the definition of novel accurate methods for feature selection. Although their stand-alone application can lead to the construction of high quality classifiers, they can be improved even more if other preprocessing techniques, such as instance selection, are considered. With the aim of enhancing the nearest neighbor ...

Nearest neighbor classifier is a widely-used effective method for multi-class problems. However, it suffers from the problem of the curse of dimensionality in high dimensional space. To solve this problem, many adaptive nearest neighbor classifiers were proposed. In this paper, a locally adaptive nearest neighbor classification method based on supervised learning style which works well for the ...