Roberto-Morales Caporal

Bio: Roberto-Morales Caporal is an academic researcher. The author has contributed to research in topics: Gene expression profiling & Selection (genetic algorithm). The author has an hindex of 1, co-authored 1 publications receiving 38 citations.

Hybrid Framework Using Multiple-Filters and an Embedded Approach for an Efficient Selection and Classification of Microarray Data

Abstract: A hybrid framework composed of two stages for gene selection and classification of DNA microarray data is proposed At the first stage, five traditional statistical methods are combined for preliminary gene selection (Multiple Fusion Filter) Then, different relevant gene subsets are selected by using an embedded Genetic Algorithm (GA), Tabu Search (TS), and Support Vector Machine (SVM) A gene subset, consisting of the most relevant genes, is obtained from this process, by analyzing the frequency of each gene in the different gene subsets Finally, the most frequent genes are evaluated by the embedded approach to obtain a final relevant small gene subset with high performance The proposed method is tested in four DNA microarray datasets From simulation study, it is observed that the proposed approach works better than other methods reported in the literature

Feature Selection and Its Use in Big Data: Challenges, Methods, and Trends

Abstract: Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions.

Cross-subject driver status detection from physiological signals based on hybrid feature selection and transfer learning

Abstract: It is a challenging and rewarding work for monitoring driving status in daily commute, which is favorable for declining the occurrence of traffic accidents and promoting driver's health. One big challenge that restricts this kind of research from real-life applications is the robustness and transfer ability of learning methods that can effectively tackle individual difference. Drawing knowledge from others through transfer learning could boost detection performance of a new driver. The present study aims to develop an efficient cross-subject transfer learning framework for driving status detection based on physiological signals. To grasp what part of knowledge was appropriate for transferring, cross-subject feature evaluation was used to measure feature quality. Then based on the evaluation score, several filtering algorithms were combined to search for better feature subsets that were not only helpful for later classification tasks but also robust to the individual difference. Finally, the framework based on hybrid feature selection and efficient transfer classifier was validated using simulated and real driving datasets. Our experimental results revealed that the proposed algorithm could achieve high recognition accuracy and good transferability among individuals, which could increase the scope of application of physiological data for drive status detection during daily life, as it alleviated the need of subject specific pilot data for assessing the physiological characteristics across subjects. This scheme can be further developed into an online warning and assistant system in vehicles helping to early detect driver's unfavorable status, better manage their negative emotion and decrease the occurrence of traffic accidents.