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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