Scholarlink

<p>Green AI aims to design and train machine learning models while taking into consideration sustainable resource usage without sacrificing model efficiency. The exponential growth of training data has led to results in increasing computational cost and energy consumption. Techniques like pruning, quantization, and knowledge distillation are used to shrink AI models. Data reduction is one of these techniques that enhances both the training speed up factor and the green AI score. To overcome these challenges, we introduce KM-DBSCAN, a new data clustering algorithm for intelligent data reduction. It aims to combine the geometric simplicity of K-Means with the density-awareness and noise resilience of DBSCAN to enhance the performance and the efficiency of data clustering for better border detection even in overlapping scenarios. The effect of data reduction has been examined on training and testing different machine learning models including SVM, MLP and CNN on six benchmark datasets which are Banana, USPS, Adult9a, Collision, Dry Bean and Melanoma. KM-DBSCAN achieved up to 90% data reduction, training speedups up to 3.6<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> to 6900<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation>, and carbon emission 0.0219 g to 5.374 g , while preserving competitive accuracy (e.g., 90.39% accuracy in melanoma classification using only 28.7% of the training data, with just 0.0061% accuracy loss and a 71.65% reduction in carbon emissions compared to training on the full dataset). These results demonstrate that KM-DBSCAN enables efficient and environmentally-conscious learning without compromising predictive performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

KM-DBSCAN: an enhanced density and centroid based border detection framework for data reduction towards green AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

查看全文
  • Mohamed Yasser AboElsaad,
  • Mohamed Farouk,
  • Hatem A. Khater

摘要

Green AI aims to design and train machine learning models while taking into consideration sustainable resource usage without sacrificing model efficiency. The exponential growth of training data has led to results in increasing computational cost and energy consumption. Techniques like pruning, quantization, and knowledge distillation are used to shrink AI models. Data reduction is one of these techniques that enhances both the training speed up factor and the green AI score. To overcome these challenges, we introduce KM-DBSCAN, a new data clustering algorithm for intelligent data reduction. It aims to combine the geometric simplicity of K-Means with the density-awareness and noise resilience of DBSCAN to enhance the performance and the efficiency of data clustering for better border detection even in overlapping scenarios. The effect of data reduction has been examined on training and testing different machine learning models including SVM, MLP and CNN on six benchmark datasets which are Banana, USPS, Adult9a, Collision, Dry Bean and Melanoma. KM-DBSCAN achieved up to 90% data reduction, training speedups up to 3.6 \(\times\) to 6900 \(\times\) , and carbon emission 0.0219 g to 5.374 g , while preserving competitive accuracy (e.g., 90.39% accuracy in melanoma classification using only 28.7% of the training data, with just 0.0061% accuracy loss and a 71.65% reduction in carbon emissions compared to training on the full dataset). These results demonstrate that KM-DBSCAN enables efficient and environmentally-conscious learning without compromising predictive performance.