Ensemble learning for data stream analysis: A survey

Ensemble learning for data stream analysis: A survey

In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary character...

Classifier Ensemble for Uncertain Data Stream Classification

Currently available algorithms for data stream classification are all designed to handle precise data, while data with uncertainty or imperfection is quite natural and widely seen in real-life applications. Uncertainty can arise in attribute values as well as in class values. In this paper, we focus on the classification of streaming data that has different degrees of uncertainty within class v...

Stream Data Mining: A Survey

A data stream is a massive, continuous and rapid sequence of data elements. Mining data streams raises new problems for the data mining community about how to mine continuous high-speed data items that you can only have one look at. Due to this reason, traditional data mining approach is replaced by systems of some special characteristics, such as continuous arrival in multiple, rapid, time-var...

Robust Ensemble Learning for Data

Data Stream Management Issues – A Survey

Traditional databases store sets of relatively static records with no pre-defined notion of time, unless timestamp attributes are explicitly added. While this model adequately represents commercial catalogues or repositories of personal information, many current and emerging applications require support for on-line analysis of rapidly changing data streams. Limitations of traditional DBMSs in s...