Feature Evolving Data Streams Using SVM Kernel in the Multi Novel Class Detection

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
There are many challenges which community faces in data mining, concerning with the data stream categorization. The four different issues of categorization viz. infinite length, concept drift, concept, development feature and development. Due to infinite length of data, it is impossible to store and use the traditional data. Many researchers focus on the issues of all of the four challenges for data stream categorization. In this system, novel class are detected by using the Gini co-efficient method and outliers are detected by using the adaptive threshold method.

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