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Knowledge Extraction From Big Data Using Feature Classification and Ensemble Learning With Granular Computing
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DOI：10.1049/sfw2/8673542.png)
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Abstract
En 中文
Managing large-scale data, particularly those with high-dimensional features, poses a significant challenge in data analysis. With the rapid growth in data generation and the increasing importance of data analysis for researchers, extracting meaningful patterns from big data has become a critical concern. Data analysts utilize data mining techniques to discover knowledge, extract patterns, and generate rules. However, the high number of features often leads to substantial computational costs, requiring advanced hardware resources. Moreover, the ability to derive interpretable classification rules in high-dimensional big data systems is essential. This paper proposes a method for knowledge extraction from big data using rule-based granular computing (GrC). Initially, the architecture extracts preliminary fuzzy rules using GrC techniques and fuzzy clustering. Subsequently, it employs ensemble learning to generate optimized Takagi-Sugeno-Kang (TSK) rules. The results indicate that effective feature selection through fuzzy clustering significantly contributes to the generation of optimal rules. Furthermore, experimental outcomes demonstrate that the proposed model achieves notable performance improvements compared to conventional algorithms such as Random Forest and standard gradient Boosting.
Keywords:
big data
granular computing
knowledge extraction
rule learning
TSK rule-based system
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