A New Approach Adapting Neural Network Classifiers to Sudden Changes in Nonstationary Environments

Business are increasingly analyzing streaming data in real time to achieve business objectives such as monetization or quality control. The predictive algorithms applied to streaming data sources are often trained sequentially by updating the model weights after each new data point arrives. When disruptions or changes in the data generating process occur, the online learning process allows the algorithm to slowly learn the changes; however, there may be a period of time after concept drift during which the predictive algorithm underperforms. This thesis introduces a method that makes online neural network classifiers more resilient to these concept drifts by utilizing data about concept drift to update neural network parameters

Data stream summarization by histograms clustering

"In this paper we introduce a new strategy for summarizing a fast changing. data stream. Evolving data streams are generated by non stationary processes which. require to adapt the knowledge discovery process to the new emerging concepts.. To deal with this challenge we propose a clustering algorithm where each cluster is. summarized by a histogram and data are allocated to clusters through a Wasserstein. derived distance. Histograms are a well known graphical tool for representing the. frequency distribution of data and are widely used in data stream mining, however,. unlike to existing methods, we discover a set of histograms where each one represents. a main concept in the data. In order to evaluate the performance of the method,. we have performed extensive tests on simulated data.