.png){kind=icon}
返回
DISCONA: distributed sample compression for nearest neighbor algorithm
.png){kind=icon}
OA
AI
DOI：10.1007/s10489-023-04482-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sample compression using epsilon-net effectively reduces the number of labeled instances required for accurate classification with nearest neighbor algorithms. However, one-shot construction of an epsilon-net can be extremely challenging in large-scale distributed data sets. We explore two approaches for distributed sample compression: one where local epsilon-net is constructed for each data partition and then merged during an aggregation phase, and one where a single backbone of an epsilon-net is constructed from one partition and aggregates target label distributions from other partitions. Both approaches are applied to the problem of malware detection in a complex, real-world data set of Android apps using the nearest neighbor algorithm. Examination of the compression rate, computational efficiency, and predictive power shows that a single backbone of an epsilon-net attains favorable performance while achieving a compression rate of 99%.
Keyword:
Distributed machine learning
Malware detection
Nearest neighbors
Sample compression
Big data
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Bioreductive deposition of palladium (0) nanoparticles onShewanella oneidensiswith catalytic activity towards reductive dechlorination of polychlorinated biphenyls钯 (0) 纳米颗粒在 Shewanella oneidensis 上的生物还原沉积，对多氯联苯的还原脱氯具有催化活性
PREAI
分享收藏Chromate lung cancer with special reference to its cell type and relation to the manufacturing process.png)
Cancer
OAAI
CancerIF0
OAAI分享收藏分享收藏
查看更多内容