Incremental learning from unbalanced data with concept class, concept drift and missing features: a review
P Kulkarni, R Ade - International Journal of Data Mining & …, 2014 - search.proquest.com
Recently, stream data mining applications has drawn vital attention from several research
communities. Stream data is continuous form of data which is distinguished by its online
nature. Traditionally, machine learning area has been developing learning algorithms that
have certain assumptions on underlying distribution of data such as data should have
predetermined distribution. Such constraints on the problem domain lead the way for
development of smart learning algorithms performance is theoretically verifiable. Real-word …
communities. Stream data is continuous form of data which is distinguished by its online
nature. Traditionally, machine learning area has been developing learning algorithms that
have certain assumptions on underlying distribution of data such as data should have
predetermined distribution. Such constraints on the problem domain lead the way for
development of smart learning algorithms performance is theoretically verifiable. Real-word …
[PDF][PDF] INCREMENTAL LEARNING FROM UNBALANCED DATA WITH CONCEPT CLASS, CONCEPT DRIFT AND MISSING FEATURES: A
P Kulkarni, R Ade - academia.edu
Recently, stream data mining applications has drawn vital attention from several research
communities. Stream data is continuous form of data which is distinguished by its online
nature. Traditionally, machine learning area has been developing learning algorithms that
have certain assumptions on underlying distribution of data such as data should have
predetermined distribution. Such constraints on the problem domain lead the way for
development of smart learning algorithms performance is theoretically verifiable. Real-word …
communities. Stream data is continuous form of data which is distinguished by its online
nature. Traditionally, machine learning area has been developing learning algorithms that
have certain assumptions on underlying distribution of data such as data should have
predetermined distribution. Such constraints on the problem domain lead the way for
development of smart learning algorithms performance is theoretically verifiable. Real-word …
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