The k nearest neighbor classification method is one of the humblest method in conceptually and it is a top method in image mining. In this work, the enhanced k nearest neighbor (EKNN) technique has been implemented to identify the cancer and automatic classification of benign and malignant tissues in the huge amount of lung cancer image datasets. In this proposed system, we have used three stag...

This paper presents a novel Nearest Neighbor (NN) classifier. NN classification is a well studied method for pattern classification having the following properties; * it performs maximum-margin classification and achieves less than the twice of ideal Bayesian error, * it does not require the knowledge on pattern distributions, kernel functions or base classifiers, and * it can naturally be appl...

This paper presents a simple and effective technique for converting handwritten textual character from paper document into machine readable form. The proposed method takes the scanned image of the handwritten character from paper document as input and shows the recognized character as its output. Using this method, the object in the converted binary image is segmented and is resized in a global...

The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classification is the process of assigning a document to one or more target categories, based on its contents. In the proposed method, machine learning methods for text classification is ...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...

This paper summarizes our research on feature selection and extraction from high-dimensionality data sets using genetic algorithms. We have developed a GA-based approach utilizing a feedback linkage between feature evaluation and classification. That is, we carry out feature selection or feature extraction simultaneously with classifier design, through “genetic learning and evolution.” This app...

The main two drawbacks of nearest neighbor based classifiers are: high CPU costs when the number of samples in the training set is high and performance extremely sensitive to outliers. Several attempts of overcoming such drawbacks have been proposed in the pattern recognition field aimed at selecting/gen-erating an adequate subset of prototypes from the training set. The problem addressed in th...