This paper evaluates and predicts a certain epidemiological (cancer survival) condition using data-mining techniques in SAS®. A data set that contains information about the survival of lung-cancer patients from a study at the Mayo Clinic was extracted from the R survival package. Data-mining techniques—namely linear and logistic regression models, regression and classification trees, and neares...

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 ...

In this article, we perform an extended analysis of different face-processing techniques for gender recognition problems. Prior research works show that support vector machines (SVM) achieve the best classification results. We will show that a nearest neighbor classification approach can reach a similar performance or improve the SVM results, given an adequate selection of features of the input...

Classification of music genre has been an inspiring job in the area of music information retrieval (MIR). Classification of genre can be valuable to explain some actual interesting problems such as creating song references, finding related songs, finding societies who will like that specific song. The purpose of our research is to find best machine learning algorithm that predict the genre of s...

The nearest neighbor classifier (NNC) is a popular non-parametric classifier. It is a simple classifier with no design phase and shows good performance. Important factors affecting the efficiency and performance of NNC are (i) memory required to store the training set, (ii) classification time required to search the nearest neighbor of a given test pattern, and (iii) due to the curse of dimensi...

K-Nearest Neighbor Classification (kNNC) makes the classification by getting votes of the k-Nearest Neighbors. Performance of kNNC is depended largely upon the efficient selection of k-Nearest Neighbors. All the attributes describing an instance does not have same importance in selecting the nearest neighbors. In real world, influence of the different attributes on the classification keeps on c...

Feature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting...

In this paper, we introduce an incremental dimensionality reduction approach for labeled data. The algorithm incrementally samples in latent space and chooses a solution that minimizes the nearest neighbor classification error taking into account label information. We introduce and compare two optimization approaches to generate supervised embeddings, i.e., an incremental solution construction ...

This paper presents a study of automatic detection and recognition of bird species based on bioacoustics. The energy distribution in different frequency bands varies quite significantly among different birds’ sounds. Energy in various frequency bands is extracted using wavelet packet transform and k-nearest neighbor algorithm is used for classification. In our experiment, classification accurac...

This paper presents a new hybrid classifier that combines the Nearest Neighbor distance based algorithm with the Classification Tree paradigm. The Nearest Neighbor algorithm is used as a preprocessing algorithm in order to obtain a modified training database for the posterior learning of the classification tree structure; experimental section shows the results obtained by the new algorithm; com...