LARSEN-ELM：使用混合数据的LARS选择性增强极限学习机
LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data
摘要：
极限学习机（ELM）作为一种神经网络算法，具有速度快、结构简单等优点，但原有ELM算法对混合数据鲁棒性不强的缺点也是不可避免的。我们提出了一个新的机器学习框架称为LARSEN-ELM克服这个问题。在我们的论文中，我们想展示LARSEN-ELM中的两个关键步骤。在第一步，预处理，我们使用最小角度回归（LARS）选择与输出高度相关的输入变量。在第二步训练中，我们使用基于遗传算法的选择性集成和原始ELM。在实验中，我们应用了两个正弦和四个数据集从UCI库，以验证我们的方法的鲁棒性。实验结果表明，与原始ELM算法以及OP-ELM、GASEN-ELM和LSBoost算法相比，LARSEN-ELM算法在保持较高速度的同时，显著提高了鲁棒性。
英文摘要：
Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
