| Summary: | Multiple classifier system has shown to be an effective technique for classification.
The success of multiple classifiers does not entirely depend on the base classifiers
and/or the aggregation technique. Other parameters, such as training data, feature
attributes, and correlation among the base classifiers may also contribute to the
success of multiple classifiers. In addition, interaction of these parameters with each other may have an impact on multiple classifiers performance. In the present study, we intended to examine some of these interactions and investigate further the effects of these interactions on the performance of classifier ensembles.
The proposed research introduces a different direction in the field of multiple
classifiers systems. We attempt to understand and compare ensemble methods from
the cooperation perspective. In this thesis, we narrowed down our focus on cooperation at training level. We first developed measures to estimate the degree and type of cooperation among training data partitions. These evaluation measures enabled us to evaluate the diversity and correlation among a set of disjoint and overlapped partitions. With the aid of properly selected measures and training information, we proposed two new data partitioning approaches: Cluster, De-cluster, and Selection (CDS) and Cooperative Cluster, De-cluster, and Selection (CO-CDS). In the end, a
comprehensive comparative study was conducted where we compared our proposed
training approaches with several other approaches in terms of robustness of their
usage, resultant classification accuracy and classification stability.
Experimental assessment of CDS and CO-CDS training approaches validates
their robustness as compared to other training approaches. In addition, this study
suggests that: 1) cooperation is generally beneficial and 2) classifier ensembles that
cooperate through sharing information have higher generalization ability compared
to the ones that do not share training information.
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