Algorithm selection model based on fuzzy multi-criteria decision in big data information mining

In the era of big data, efficient classification of rapidly growing data volumes is a critical challenge. Traditional algorithms often fall short in handling the scale and complexity of big data, leading to inefficiencies in classification accuracy and processing times. This study aims to address these limitations by introducing a novel approach to algorithm selection, which is essential for advancing big data classification methods. We developed an advanced classification algorithm that integrates a fuzzy multi-criteria decision-making (MCDM) model, specifically tailored for big data environments. This integration involves leveraging the analytical strengths of MCDM, particularly the analytic hierarchy process, to systematically evaluate and select the most suitable classification algorithms. Our method uniquely combines the precision of fuzzy logic with the comprehensive evaluative capabilities of MCDM, setting it apart from conventional approaches. The proposed model is meticulously designed to assess key performance indicators such as accuracy, true rate, and processing efficiency in various big data scenarios. Our findings reveal that the proposed model significantly enhances classification accuracy and processing efficiency compared to traditional algorithms. The model demonstrated a marked improvement in true rates and overall classification performance, showcasing its effectiveness in handling large-scale data challenges. These results underline the model's potential as a pragmatic solution for big data classification, offering substantial improvements over existing methodologies. The study contributes a groundbreaking perspective to the field of big data classification, addressing critical gaps in current practices. By combining fuzzy logic with MCDM, the proposed model offers a more nuanced and effective approach to algorithm selection, catering to the intricate demands of big data environments. This research not only enhances the understanding of classification behaviors in big data but also paves the way for future advancements in data mining technologies. Its implications extend beyond theoretical value, providing practical tools for practitioners and researchers in the realm of big data analytics.