Verónica Bolón-Canedo

The growth in the number of wearable devices has increased the amount of data produced daily. Simultaneously, the limitations of such devices has also led to a growing interest in the implementation of machine learning algorithms with low‐precision computation. We propose green and efficient modifications of state‐of‐the‐art feature selection methods based on information theory and fixed‐point representation. We tested two potential improvements: stochastic rounding to prevent information loss, and logarithmic division to improve computational and energy efficiency. Experiments with several datasets showed comparable results to baseline methods, with minimal information loss in both feature selection and subsequent classification steps. Our low‐precision approach proved viable even for complex datasets like microarrays, making it suitable for energy‐efficient internet‐of‐things (IoT) devices. While further investigation into stochastic rounding did not yield significant improvements, the use of logarithmic division for probability approximation showed promising results without compromising classification performance. Our findings offer valuable insights into resource‐efficient feature selection that contribute to IoT device performance and sustainability.

As benchmark image datasets expand in sample size and feature complexity, the challenge of managing increased dimensionality becomes apparent. Contrary to the expectation that more features equate to enhanced information and improved outcomes, the curse of dimensionality often hampers performance. This paper reviews existing literature on filter feature selection techniques applied to image features, highlighting when they are applied to both classical and deep-learning-based feature extraction methods. Additionally, this study explores how different feature selection methods behave when applied to image features through big data technologies. Different experiments were performed to compare the results when using feature selection techniques with various reduction percentages. Experimental results demonstrated that an important reduction of the extracted features provides classification results similar to those obtained with the full set of features. Furthermore, applying dimensionality reduction techniques outperforms, in some cases, the results achieved using broad feature vectors.

A common situation in classification tasks is to deal with unbalanced datasets, an issue that appears when the majority class(es) has a large number of samples compared to the minority class(es). This problem is even more significant when the datasets have a large number of features but only a few samples, as is the case with microarray datasets. Traditionally, an approach to alleviate this problem has been the application of sampling methods to obtain more balanced classes, increasing the number of samples in the minority class (replicating samples or generating new synthetic samples), or decreasing the number of samples in the majority class. In this study, we have compared different balancing methods, including a novel method that applies sampling in both the minority and majority classes. The interest in applying feature selection in combination with balancing methods has also been explored. In view of the results, a recommendation of sampling method, feature selection, and classifier is proposed to improve the classification results according to the type of dataset.