G. Sunil Santhosh Kumar1,2,*, M. Rudra Kumar3
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 695-713, 2024, DOI:10.32604/cmc.2024.050143
- 18 July 2024
Abstract This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost, a machine learning algorithm renowned for its efficiency and performance. The framework proposed herein utilizes the fusion of diversified feature formats, specifically, metadata, textual, and pattern features. The goal is to enhance the system’s ability to discern and generalize transformation rules from source to destination formats in varied contexts. Firstly, the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model. Subsequent sections expound… More >
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