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Multiple classifier system (MCS) has become a successful alternative forimproving classification performance. However, studies have shown inconsistentresults for different MCSs, and it is often difficult to predict which MCSalgorithm works the best on a particular problem. We believe that the twocrucial steps of MCS - base classifier generation and multiple classifiercombination, need to be designed coordinately to produce robust results.  In this work, we show that for different testing instances, betterclassifiers may be trained from different subdomains of training instancesincluding, for example, neighboring instances of the testing instance, or eveninstances far away from the testing instance. To utilize this intuition, wepropose Individualized Classifier Ensemble (ICE). ICE groups training data intooverlapping clusters, builds a classifier for each cluster, and then associateseach training instance to the top-performing models while taking into accountmodel types and frequency. In testing, ICE finds the k most similar traininginstances for a testing instance, then predicts class label of the testinginstance by averaging the prediction from models associated with these traininginstances.  Evaluation results on 49 benchmarks show that ICE has a stable improvement ona significant proportion of datasets over existing MCS methods. ICE provides anovel choice of utilizing internal patterns among instances to improveclassification, and can be easily combined with various classification modelsand applied to many application domains.

A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction