# Framework for the Ensemble of Feature Selection Methods
## 1 Introduction
### Dataset's Growth  
Recently, datasets with large numbers of features are more frequent in different domains. Three of the most representative examples are microarray classification, text categorization, and signal classification. In the first case, developments in DNA microarray have generated numerous datasets with this kind of data. In most of these datasets, the number of instances is not higher than 100 (patients), and the number of features (genes) ranges from 6.000 to 60.000 [7]. However, previous studies showed that most of the genes in these datasets do not represent helpful information to support a machine learning process. Consequently, a preprocessing stage is needed to efficiently classify microarray data [8; 9]. In [10; 11], the authors describe how to reduce computational cost and improve performance in the classification of microarrays by selecting a representative subset of genes from the original set.  
Likewise, in-text categorization documents are represented by an array built from their vocabulary and the frequencies of words in such documents. Those vocabulary sets have hundreds of thousands of words. However, vocabulary is pruned to remove the least essential words from the documents in an initial stage. Thus, the size of the array that represents the documents is reduced. In literature, there are several collections of documents used in different application domains, for instance, email analysis [12], detection of articles related to terrorism on the web [13], automatic classification of text [14], opinions [13], feelings [15; 16], and emotions [17], among others. These collections have between 5000 and 800,000 documents.  
In the field of signal classification, previous works have used many mechanisms to process the signal and get a set of features capable of describing the signal. These features are used to classify or detect patterns. In the medical domain, the high availability of devices designed to capture biosignals has supported the diagnosis of diseases by identifying normal and abnormal patterns in the signals. Hence, several authors have developed solutions to support the automatic analysis of signals such as EEG and ECG. In the automated analysis of EEGs, for instance, the signals considered are multichannel with (i) information in channels, which range from 12 to 64 in number, (ii) duration between 20 min and 72 h, and (iii) a sampling rate between 100 and 256 per second. Considering the above, analyzing an EEG signal is a complex task since it contains much information. Thus, each channel from the signal is divided into segments, and many feature extractors must be applied to describe them. The process to segment each channel of the signal allows the identification of abnormalities, which appear in short periods [18].  
In [19], the authors analyzed the current context of \"Big Data\" and \"Big Dimensionality.\" They introduced those concepts to explain how to handle datasets with unbalanced data, noise, few instances, and a high number of features. They found out that the dataset sizes are not growing in both dimensions, columns, and rows. Besides, the most important repositories of datasets used in the experiments of machine learning contain datasets with thousands or millions of features. In many cases, the number of features widely surpasses the number of rows. For example, in the UCI machine learning repository [20], there are 18 datasets with more than 5000 features. The LIBSVM database contains datasets with over a million features [21]. Therefore, researchers have focused on developing methods to reduce the size of datasets using a set of objective criteria. This allows them to represent the complex original dataset in a simple dataset.