Helyane Bronoski Borges

Attribute selection methods comparison for classification of diffuse large B-cell lymphoma

The use of data mining techniques has helped to solve many problems in the rapidly growing field of bioinformatics. Despite that, the presence of thousands of attributes makes the results unclear and also contributes to the decrease of the accuracy of the classifier used. This paper presents a comparison of the use of various attribute selection methods aiming to reduce the number of genes to be searched. The results show that most of the combinations from search algorithms and evaluation algorithms within the attribute selection algorithm work well, reducing the number of attributes and leading to improved classification rates.

Multi-Label Hierarchical Classification using a Competitive Neural Network for protein function prediction

Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents an algorithm for hierarchical classification using the global approach, called Multilabel Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested on some datasets from the bioinformatics field and its results are promising.

Comparing the dimensionality reduction methods in gene expression databases

Dimensionality reduction has been applied in the most different areas, among which the data analysis of gene expression obtained with the microarray approach. The data involved in this problem is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of attribute selection and random projection method in microarray data. Experimental results are promising and show that the use of these methods improves the performance of classification algorithms.

Hierarchical classification using a Competitive Neural Network

Hierarchical classification is a problem with application in many areas. Therefore, it makes the development of algorithms able to induce hierarchical classification models. This paper presents an algorithm for hierarchical classification using the global approach, called Hierarchical Classification using a Competitive Neural Network (HC-CNN). It was tested on some datasets from the bioinformatics field and the results are promising.

An evaluation of global-model hierarchical classification algorithms for hierarchical classification problems with single path of labels

Several classification tasks in different application domains can be seen as hierarchical classification problems. In order to deal with hierarchical classification problems, the use of existing flat classification approaches is not appropriate. For these reason, there has been a growing number of studies focusing on the development of novel algorithms able to induce classification models for hierarchical classification problems. In this paper we study the performance of a novel algorithm called Hierarchical Classification using a Competitive Neural Network (HC-CNN) and compare its performance against the Global-Model Naive Bayes (GMNB) on eight protein function prediction datasets. Interestingly enough, the comparison of two global-model hierarchical classification algorithms for single path of labels hierarchical classification problems has never been done before.

Gene Selection from Microarray Data

This chapter presents the use of attribute selection techniques to define a subset of genes related to specific characteristics such as cancer arising. Through combination of search methods and evaluation procedures, it is showed that the data mining algorithm speeds up, mining performance such as predictive accuracy is improved and the comprehensibility of the results becomes easier in most of the combinations. Best results were achieved with wrapper approaches and sequential search.

Feature Selection as a Preprocessing Step for Classification in Gene Expression Data

Many times, when studying gene expression data, unknown attributes, which can be redundant and even, in certain cases, irrelevant, are manipulated. The application of selection attributes algorithms as a preprocessing can help in the knowledge discovery database process. This paper is about applying selection attributes algorithms in two gene expression databases. The result shows that the use of these algorithms can improve the classification algorithms performance.

Dimensionality Reduction in Gene Expression Database through the Random Projection Method

Dimensionality reduction applied to gene expression is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of random projection method in microarray data. Experimental results are promising and it shows that the use of this method improves the performance of classification algorithms.