Search Results

This paper presents a generative modeling approach called Gmda designed for tabular data, adapted to its arbitrary feature correlation structure. The generative model is trained so that sampled regions in the feature space contain the same ...

This paper focuses on binary classification with reject option, enabling the classifier to detect and abstain hazardous decisions. While reject classification produces in more reliable decisions, there is a tradeoff between accuracy and rejection ...

• A model of cascade of classifiers with reject option
• Optimization of the reject area of the classifiers
• Ranking of the features for the cascade construction
• Minimization of the cost of classification
• Controlling the trade-...

Display Omitted

Although the prediction performance is crucial for a classifier, its cost of use is also an essential issue for practical application. The aim of this article is to propose a prediction method that controls not only the error rate but also the ...

• Analysis of three different visualization spaces to represent the performance of reject classifiers.

The classification with reject option consists to train a classifier that rejects the examples when the confidence in its prediction is low. The objective is to improve the accuracy of the non-rejected examples and the reliability of ...

In many machine learning problems, the performance of the results is measured by indices that often combine precision and recall. In this paper, we study the behavior of such indices in function of the tradeoff precision-recall. We present a new ...

The supervised learning in bioinformatics is a major tool to diagnose a disease, to identify the best therapeutic strategy or to establish a prognostic. The main objective in classifier construction is to maximize the accuracy in order to obtain a ...

This paper focuses on binary classification with reject option, enabling the classifier to detect and abstain hazardous decisions. While reject classification produces in more reliable decisions, there is a tradeoff between accuracy and rejection rate. ...

Feature selection is an important step when building a classifier. However, the feature selection tends to be unstable on high-dimension and small-sample size data. This instability reduces the usefulness of selected features for knowledge discovery: if ...

The ensemble methods are very popular and can improve significantly the performance of classification and clustering algorithms. Their principle is to generate a set of different models, then aggregate them into only one. Recent works have shown that ...

Feature selection is an important step when building a classifier on high dimensional data. As the number of observations is small, the feature selection tends to be unstable. It is common that two feature subsets, obtained from different datasets but ...

Biclustering is a major tool of data mining in many domains and many algorithms have emerged in recent years. All these algorithms aim to obtain coherent biclusters and it is crucial to have a reliable procedure for their validation. We point out the ...

Error estimation accuracy is the salient issue regarding the validity of a classifier model. When samples are small, training-data-based error estimates tend to suffer from inaccuracy and quantification of error estimation accuracy is difficult. ...

Gene selection is a crucial step when building a classifier from microarray or metagenomic data. As the number of observations is small, the gene selection tends to be unstable. It is common that two gene subsets, obtained from different datasets but ...

Several biclustering algorithms have been proposed in different fields of microarray data analysis. We present a new approach that improves their performance in using the ensemble methods. An ensemble biclustering is considered and formalized by a ...

One of the major aims of many microarray experiments is to build discriminatory diagnosis and prognosis models. A large number of supervised methods have been proposed in literature for microarray-based classification for this purpose. Model evaluation ...

Biclustering is become undoubtedly a current tool for micro array data analysis. Its objective is to identify a set of biclusters, i.e. sub matrices of the original data matrix, presenting a particular pattern. A large number of biclustering methods ...

Several methods have been proposed for microarray data analysis that enables to identify groups of genes with similar expression profiles only under a subset of examples. We propose to improve the performance of these biclustering methods by adapting ...

One of the major tools of transcriptomics is the biclustering that simultaneously constructs a partition of both examples and genes. Several methods have been proposed for microarray data analysis that enables to identify groups of genes with similar ...

One of the major tools of transcriptomics is the biclustering that simultaneously constructs a partition of both examples and genes. Several methods have been proposed for microarray data analysis that enables to identify groups of genes with similar ...

One of the major tools of transcriptomics is the biclustering that simultaneously constructs a partition of both examples and genes. Several methods have been proposed for microarray data analysis that enables to identify groups of genes with similar ...