.png){kind=icon}
返回
Self-Tune Linear Adaptive-Genetic Algorithm for Feature Selection
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2942962.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Genetic algorithm (GA) is an established machine learning technique used for heuristic optimisation purposes. However, this natural selection-based technique is prone to premature convergence, especially of the local optimum event. The presence of stagnant performance is due to low population diversity and fixed genetic operator setting. Therefore, an adaptive algorithm, the Self-Tune Linear AdaptiveGA (STLA-GA), is presented in order to avoid suboptimal solutions in feature selection case studies. STLA-GA performs parameter tuning for mutation probability rate, population size, maximum generation number and novel convergence threshold while simultaneously updating the stopping criteria by adopting an exploration-exploitation cycle. The exploration-exploitation cycle embedded in STLA-GA is a function of the latest classifier performance. Compared to standard feature selection practice, the proposed STLA-GA delivers multi-fold benefits, including overcoming local optimum solutions, yielding higher feature subset reduction rates, removing manual parameter tuning, eliminating premature convergence and preventing excessive computational cost, which is due to unstable parameter tuning feedback.
Keyword:
Classification
exploration-exploitation cycle
feature selection
parameter tuning
STLA-GA
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Facile Synthesis of Indium Sulfide/Flexible Electrospun Carbon Nanofiber for Enhanced Photocatalytic Efficiency and Its Application.png)
ScanningOAAI
ScanningIF0
OAAI分享收藏Direct photographic documentation of ileal mucosa in routine colonoscopy is not an independent valid or reliable proof of completion: quality assurance issues for the national colorectal cancer‐screening programme常规结肠镜检查中回肠黏膜的直接摄影记录并非完成检查的独立有效或可靠证据：国家结直肠癌筛查计划的质量保证问题
PREAI
分享收藏
分享收藏Evolving rule-based classifiers with genetic programming on GPUs for drifting data streamsPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容