Review of Polynomial Learning Time for Concept-Drift Data in Streaming Environment Based on Discard-After-learn Concept
Keywords:
discard-after-learn, streaming data classification, concept drift, non-stationary environments, hyper-ellipsoid functionAbstract
Concept drift or class drift is the situation where some whole learning data chunk occasionally change their classes at different times based the known conditional probability between the data and their classes. Some applications concerning the class drift are cybersecurity, surgery prediction, weather prediction. The class drift usually occurs in a data streaming environment. Learning, classifying, identifying classes of classdrift data in this environment are challenging problems in machine intelligence. Several methods were proposed to detect the occurrence of drift rather than to learning class-drifted data as well as querying their classes. Furthermore, the computational time complexity in those studies and memory overflow due to streaming scenario are not seriously focused. This paper reviews the series of polynomial space and time complexities of learning algorithms to handle class-drifted data in streaming environment based on the concept of discard-after-learn. A new neural network structure and the theorems of recurrence functions to compute the center, eigenvectors, and eigenvalues of the structure are summarized.
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