The aim of this research paper is the classification of environmental datasets as belonging to either North America or South America using KNN classifier for which different texture features are utilized to characterize the information level contained in the image. The proposed method is compared with...
This paper introduces a document organization application of text categorization, using k-Nearest Neighbor (k-NN) classification. The method makes use of training documents, which have known categories, and finds the closest neighbors of the new sample document among all. These neighbors enables to ...
And k-NN classification is highly sensitive to the local geometry of the training data.When I have a classification problem with strictly numeric predictor values, and a non-huge set of training data (for example, less than one million items), using k-NN is often my first approach. Then ...
Several datasets are used to evaluate the performance of the proposed DE based metrics and to compare it to some k-NN variants from the literature. Practical experiments indicate that in most cases, incorporating DE in k-NN classification can provide more accurate performance. 展开 ...
The model uses four ML classifiers, namely: (i) Decision Tree (DT)30; (ii) Support Vector Machine (SVM)31; (iii) K-Nearest Neighbour (KNN)32; and (iv) Ensemble Classifier (EC)33. These classifiers are applied for oil sample classification and are selected based on their capacity to ...
The weighted k-NN classification algorithm has received increased attention recently for two reasons. First, by using neural autoencoding, k-NN can deal with mixed numeric and non-numeric predictor values. Second, compared to many other classification algorithms, the results of weighted k-NN ar...
We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-bas...
Automatic classification of computed tomography brain images using ANN, k-NN and SVM Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagn... NH Rajini,R Bhavani - 《Ai & Society》 被引量: 1...
Weighted k-NN Classification Using C# By James McCaffrey The goal of a machine learning (ML) classification problem is to predict a discrete value. For example, you might want to predict the political leaning (conservative, moderate, liberal) of a person based on their age, annual health care...
The k-nearest neighbor algorithm (k-NN) is a widely used machine learning algorithm used for both classification and regression. k-NN algorithms are used in many research and industrial domains such as 3-dimensional object rendering, content-based image retrieval, statistics (estimation of entropie...