=Paper=
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|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1924/ialatecml_3_invited_talk.pdf
|volume=Vol-1924
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1924/ialatecml_3_invited_talk.pdf</pdf>
<pre>
              Invited Talk :
Ensemble Learning from Data Streams with
 Active and Semi-Supervised Approaches

                            Bartosz Krawczyk

                    Department of Computer Science
           Virginia Commonwealth University, Richmond, VA
                          bkrawczyk@vcu.edu



 Abstract. Developing efficient classifiers which are able to cope with big
 and streaming data, especially with the presence of the so-called concept
 drift is currently one of the primary directions among the machine le-
 arning community. This presentation will be devoted to the importance
 of ensemble learning methods for handling drifting and online data. It
 has been shown that a collective decision can increase classification accu-
 racy due to mutually complementary competencies of each base learner.
 This premise is true if the set consists of diverse and mutually comple-
 mentary classifiers. For non-stationary environments, diversity may also
 be viewed as a changing context which makes them an excellent tool
 for handling data shifts. The main focus of the lecture will be given to
 using these mentioned advantages of ensemble learning for data stream
 mining on a budget. As streaming data is characterized by both massive
 volume and velocity one cannot assume unlimited access to class labels.
 Instead methods that allow to reduce the number of label queries should
 be sought after. Recent trends in combining active and semi-supervised
 learning with ensemble solutions, such as online Query by Committee or
 Self-Labeling Committees, will be presented. Additionally, this talk will
 offer discussion on emerging challenges and future directions in this area.




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