Abstract-This paper focuses on reducing the interference effect among input attributes. When training different attributes together, there may exist negative effect among them due to interference. To reduce the interference, input attributes are placed into different groups such that attributes with no interference with each other are placed in the same group. Two types of grouping strategies are examined in this paper, i.e. non-overlapping and overlapping. To further enhance the performance, multiple learners are employed to tackle different groups. Three integration methods i.e. voting, weighting and result-integration network (RIN) are examined.It turns out that the result-integration network has the best performance, followed by weighting and then voting. The ensemble approach can improve the performance of neural-network learning. Such an approach also can be employed with feature selection to further enhance the performance.

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