Evolving Memristive Reservoir.

In light of the dynamic plasticity, nanosize, and energy efficiency of memristors, memristive reservoirs have attracted increasing attention in diverse fields of research recently. However, limited by deterministic hardware implementation, hardware reservoir adaptation is hard to realize. Existing evolutionary algorithms for evolving reservoirs are not designed for hardware implementation.

OSNN: An Online Semisupervised Neural Network for Nonstationary Data Streams.

Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of target functions and data distributions over time (concept drifts). Most existing work relies solely on labeled data to adapt to concept drifts in classification problems.

A Diversity Framework for Dealing With Multiple Types of Concept Drift Based on Clustering in the Model Space.

Data stream applications usually suffer from multiple types of concept drift. However, most existing approaches are only able to handle a subset of types of drift well, hindering predictive performance. We propose to use diversity as a framework to handle multiple types of drift.

Detecting asthma exacerbations using daily home monitoring and machine learning.

Objective: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to detect severe asthma exacerbations using easily available daily monitoring data.

A Systematic Study of Online Class Imbalance Learning With Concept Drift.

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist.