Jesús Alcalá-Fdez

TL;DR: This paper proposes to incorporate some mechanisms based on monotonicity indexes for addressing such problems in two popular and competitive evolutionary fuzzy systems algorithms for classification and regression tasks: FARC-HD and FSmogfs.

TL;DR: A novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED) and proves the goodness of this strategy for the mining of biologically relevant gene-gene temporal relations.

TL;DR: This paper presents a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules and results over two real-world databases are showed.

TL;DR: FARLAT-LQD is proposed, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data and a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth- LQD, to efficiently mine the fuzzy associationrules from inaccurate data considering the learned membership functions in the genetic process.

TL;DR: The results obtained in the experimental study performed on five Big Data problems prove the capability of MRQAR to obtain reduced set of high quality rules in reasonable time, and validate the generic MapReduce framework proposed in this work.