ABSTRACT

Nowadays, many machine learning procedures are available on the shelve and may be used easily to calibrate predictive models on supervised data. However, when the input data consists of more than one unknown cluster, linked to different underlying predictive models, fitting a model is a more challenging task. We propose, in this paper, a three-step procedure to automatically solve this problem. The first step aims at catching the clustering structure of the input data, which may be characterized by several statistical distributions. For each partition, the second step fits a specific predictive model based on the data in each cluster. The overall model is computed by a consensual aggregation of the models corresponding to the different partitions. A comparison of the performances on different simulated and real data assesses the excellent performance of our method in a large variety of prediction problems.

摘要

如今，货架上有许多机器学习程序，可以轻松地用于校准受监督数据的预测模型。然而，当输入数据由多个未知集群组成，并链接到不同的底层预测模型时，拟合模型是一项更具挑战性的任务。我们在本文中提出了一个三步程序来自动解决这个问题。第一步旨在捕捉输入数据的聚类结构，其特征可能是几个统计分布。对于每个分区，第二步根据每个集群中的数据拟合特定的预测模型。整体模型是由对应于不同分区的模型的共识聚合计算的。