Bio-inspired technique for improving machine learning speed and big data processing

Big data analytics (BDA) is progressively becoming a popular practice implemented by many organizations, because of its potential to discover treasured insights for improved decision-making. Machine Learning (ML) algorithms are one of the effective tools used for BDA, however, their computational complexity increases with an increase in data size. Therefore, this paper introduces a boundary detection and instance selection technique for improving the speed of ML-based big data classification models. The proposed technique (called ACOISA_ML) is inspired by edge selection in ant colony optimization. ACOISA_ML is evaluated on five ML algorithms and ten large- or medium-scale datasets, and the results show that it has the potential to reduce the training speed of ML algorithms by over 94% without significantly affecting their prediction accuracy. Moreover, the results show that it reduced the storage size of big datasets by over 55% (in most cases), thus improving the speed of big data processing.