Abstract
Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.
Highlights
In machine learning, multimodality refers to the simultaneous use of different information sources to solve a specific problem [1]
This section reports the results obtained with the Brain–Computer Interface (BCI) and the Magnetic Resonance Imaging (MRI) datasets
As in the tests applied to the BCI dataset, a prediction process was performed on the test data while making a sequential reduction in the number of information sources after completing the training stage and obtaining the optimal values for the free parameters
Summary
Multimodality refers to the simultaneous use of different information sources to solve a specific problem [1]. It is applied to improve some aspects of algorithms, such as the feature generation process or separation between classes, referring to the machine learning area. The most similar approach to the automatic selection of relevant information sources is feature selection through machine learning methods [9]. The studies that apply group feature selection usually identify and distinguish the information sources in a specific research area, such as electrodes in a Brain–Computer Interface (BCI) system [12] or different frequency bands in multispectral and hyperspectral imaging [13]
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