Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that ...
This paper proposes a novel approach to the problem of training classifiers to detect and correct grammar and usage errors in text by selectively introducing mistakes into the tra...
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
Analyzing the quality of data prior to constructing data mining models is emerging as an important issue. Algorithms for identifying noise in a given data set can provide a good me...
Jason Van Hulse, Taghi M. Khoshgoftaar, Haiying Hu...
Many researchers have used lexical networks and ontologies to mitigate synonymy and polysemy problems in Question Answering (QA), systems coupled with taggers, query classifiers,...