Drift-Aware Ensemble Regression
نویسندگان
چکیده
Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniques that the data set used for parameter estimation comes from a stationary joint distribution may not hold in this context because manufacturing processes are subject to physical changes like wear and aging, denoted as process drift. This can cause the estimated model to deviate significantly from the current state of the modeled system. In this paper, we discuss the problem of estimating regression models from drifting processes and we present ensemble regression, an approach that maintains a set of regression models—estimated from different ranges of the data set—according to their predictive performance. We extensively evaluate our approach on synthetic and real-world data.
منابع مشابه
Advances on Concept Drift Detection in Regression Tasks Using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classification task but usually maintain a fixed ...
متن کاملModel Averaging via Penalized Regression for Tracking Concept Drift
A supervised learning algorithm aims to build a prediction model using training examples. This paradigm typically has the assumptions that the underlying distribution and the true input-output dependency does not change. However, these assumptions often fail to hold, especially in data streams. This phenomenon is known as concept drift. We propose a new model combining algorithm for tracking co...
متن کاملDetecting Concept Drift in Data Stream Using Semi-Supervised Classification
Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...
متن کاملLearning from Data Streams with Concept Drift
Increasing access to incredibly large, nonstationary datasets and corresponding demands to analyse these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of real-world data streams is " concept drift, " whereby the distributions underlying the data can change arbitrarily over time. The presence of concept drift in a d...
متن کاملOnline tree-based ensembles and option trees for regression on evolving data streams
The emergence of ubiquitous sources of streaming data has given rise to the popularity of algorithms for online machine learning. In that context, Hoeffding trees represent the state-of-the-art algorithms for online classification. Their popularity stems in large part from their ability to process large quantities of data with a speed that goes beyond the processing power of any other streaming...
متن کامل