Add to ORKGAggregated classification trees have gained recognition due to improved stability, and frequently reduced bias. However, the adaptation of this approach to the k nearest neighbors method (kNN), faces some difficulties: the relatively high stability of these classifiers, and an increase of misclassifications when the variables without discrimination power are present in the training set. In this paper we propose aggregated kNN classifier with feature selection. Its classification accuracy has been verified on the real data with added irrelevant variables
k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable performan...
The performance of many learning and data mining algorithms depends critically on suitable metrics t...
One of the ways of increasing recognition ability in classification problem is removing outlier entr...
Combining multiple classifiers, known as ensemble methods, can give substantial improvement in predi...
Combining multiple classifiers can give substantial improvement in prediction performance of learnin...
In a classification problem with binary outcome attribute, if the input attributes are both continuo...
Feature combination is a powerful approach to improve object classification performance. While vario...
Due to the growing amount of data generated and stored in relational databases, relational learning ...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
The k Nearest Neighbors (kNN) method is a widely used technique to solve classification or regressio...
Abstract. Since kNN classifiers are sensitive to outliers and noise con-tained in the training data ...
We present a novel method that aims at providing a more stable selection of feature subsets when var...
To minimize the effect of outliers, kNN ensembles identify a set of closest observations to a new sa...
Rough set theories are utilized in class-specific feature selection to improve the classification pe...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable performan...
The performance of many learning and data mining algorithms depends critically on suitable metrics t...
One of the ways of increasing recognition ability in classification problem is removing outlier entr...
Combining multiple classifiers, known as ensemble methods, can give substantial improvement in predi...
Combining multiple classifiers can give substantial improvement in prediction performance of learnin...
In a classification problem with binary outcome attribute, if the input attributes are both continuo...
Feature combination is a powerful approach to improve object classification performance. While vario...
Due to the growing amount of data generated and stored in relational databases, relational learning ...
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i...
The k Nearest Neighbors (kNN) method is a widely used technique to solve classification or regressio...
Abstract. Since kNN classifiers are sensitive to outliers and noise con-tained in the training data ...
We present a novel method that aims at providing a more stable selection of feature subsets when var...
To minimize the effect of outliers, kNN ensembles identify a set of closest observations to a new sa...
Rough set theories are utilized in class-specific feature selection to improve the classification pe...
The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet...
k nearest neighbor (kNN) is a simple and widely used classifier; it can achieve comparable performan...
The performance of many learning and data mining algorithms depends critically on suitable metrics t...
One of the ways of increasing recognition ability in classification problem is removing outlier entr...