Ensemble Methods grows along with Machine Learning and Computational Intelligence
domain proves to be effective and versatile. It helps in reducing variance and improves …

Accepted: 10/May/2019, Published: 31/May/2019 Abstract—incurrent era of big data,
thedata analytics has become more challenging issue. Data get mined for finding facts as …

Stream data are sequence of data examples that continuously arrive at time-varying and
possibly unbound streams. These data streams are potentially huge in size and thus it is …

Data stream classification is the process of learning supervised models from continuous
labelled examples in the form of an infinite stream that, in most cases, can be read only once …

Mining in data stream plays a vital role in Big Data analytics. Traffic management, sensor
networks and monitoring, weblogs analysis are the application of dynamic environments …

Abstract The concept of Data Stream has emerged as a result of the evolution of
technologies in different domains such as banking, e-commerce, social media, and many …

Data streams are viewed as a sequence of relational tuples (eg, sensor readings, call
records, web page visits) that continuously arrive at time-varying and possibly unbound …

In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

The data in streaming environment tends to be non-stationary. Hence, frequent and irregular
changes occur in data, which usually denotes as a concept drift related to the process of …

The tremendous amount of data is generated regularly through areas like networking,
telecommunication, stock market, satellite, weather forecasting, etc. So, the classification …