Research Article
Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams
| Input: The instances , | | the pool maximum limit , and | | the smoothing parameter . | | Output: The pool of microcluster | (1) | the pool of initial microclusters which is formed by -means | (2) | for each instance do | | Phase 1: Classification | (3) | distance between and | (4) | select the k-nearest microclusters to classify the instance | (5) | the predicted class label of instance gained by majority vote in equation (5) | (6) | update the parameter of the k-nearest microcluster | | Phase 2: Incremental Learning | (7) | if Scenario 1 then | (8) | update the structure of nearest microcluster by equations (1)–(3) and the number of the instances in microcluster will be incremented by 1 | (9) | else if Scenario 2 then | (10) | consider the instance as a noisy point and neglect it | (11) | else if Scenario 3 then | (12) | build a new microcluster on instance | | Phase 3: Updating Pool | (13) | if then | (14) | | (15) | | (16) | else | (17) | the worst microcluster | (18) | replace | (19) | end if | (20) | end if | (21) | end for | (22) | return microcluster pool at required time stamp |
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