A Survey on Multi-Label Data Stream Classification

Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.” In existing system, we are implementing a large number of streaming data such as web click data streams and sensor network data, the speed with which credit card transactions can be named. 

Currently we use static dataset for data streaming, this dataset is the opposite of giving data mining format because it has less memory and can only perform the action on time. In this application, we provide a comprehensive review of existing multi-label streams mining algorithms and categorize these methods based on different perspectives, which mainly focus on the multi-label data stream classification. We first briefly summarize existing multi-label and data stream classification algorithms and discuss their merits and demerits. Secondly, we identify mining constraints on classification for multi-label streaming data, and present a comprehensive study in algorithms for multi-label data stream classification. Finally, several challenges and open issues in multi-label data stream classification are discussed, which are worthwhile to be pursued by the researchers in the future.

Keywords: Multi Label, Data Mining, Data Stream Mining, Multi Label Classification.