This work deals with the problem of classifying uncertain data. With this aim the Uncertain Nearest Neighbor (UNN) rule is here introduced, which represents the generalization of the deterministic nearest neighbor rule to the case in which uncertain objects are available. The UNN rule relies on the concept of nearest neighbor class, rather than on that of nearest neighbor object. The nearest ne...

Object recognition is a research area that aims to associate objects to categories or classes. The recognition of object specific geospatial features, such as roads, buildings and rivers, from high-resolution satellite imagery is a time consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Feature selection is the task of selecting a small subset fro...

We discuss the role of nearest neighbor The nearest neighbor method can essential be seen technologies in case based reasoning. We describe the to involve a two step process. The first step in the use of the ordered weighted averaging for the nearest neighbor approach is to calculate the similarity development of nearest neighbor rules. A procedure for of the object of interest to each of the o...

A watershed-based segmentation approach that uses iterative bilateral smoothing with an adapting photometric similarity parameter for pre-filtering is adopted for the segmentation of rock scenes. But the resultant segmented images also contain non-rock watershed regions which are not desired for measuring rock sizes. A proximity-based classifier is applied for the removal of the latter using fe...

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated...

In this paper we introduced Tanimoto based similarity measure for host-based intrusions using binary feature set for training and classification. The k-nearest neighbor (kNN) classifier has been utilized to classify a given process as either normal or attack. The experimentation is conducted on DARPA-1998 database for intrusion detection and compared with other existing techniques. The introduc...

For many years, reading rate as word correct per minute (WCPM) has been investigated by researchers an indicator of learners’ level oral speed, accuracy, and comprehension. The aim the study is to predict levels WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), K- Nearest Neighbor (KNN). data this were collected from 100 Kurdish EFL students in...

This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which s...