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Improving Hyperspectral Pixel Classification With Unsupervised Training Data Selection
(Institute of Electrical and Electronics Engineers (IEEE), 2014)
An unsupervised method for selecting training data is suggested here. The method is tested by applying it to hyperspectral land-use classification. The data set is reduced using an unsupervised band selection method and ...
On measures of dissimilarity between point patterns: Classification based on prototypes and multidimensional scaling
(Wiley, 2015-03)
This paper presents a collection of dissimilarity measures to describe and then classify spatial point patterns when multiple replicates of different types are available for analysis. In particular, we consider a range of ...
A literature review on the application of evolutionary computing to credit scoring
(Palgrave Macmillan, 2013)
The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical ...
Mapping microarray gene expression data into dissimilarity spaces for tumor classification
(Elsevier, 2015-02)
Microarray gene expression data sets usually contain a large number of genes, but a small
number of samples. In this article, we present a two-stage classification model by combining
feature selection with the ...
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
(Elsevier, 2012)
The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning ...