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Summary: Objective: We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. Methods and material: Gene expression based cancer classification is used as the task in this study. Six gene expression datasets containing different types of cancer constitute test data. Results: Through extensive simulation coupled with the copula method for analysis of association in bivariate data, we show that dataset complexity and bolstered resubstitution error are associated in terms of dependence. As a result, we propose a new scheme for generating ensembles of classifiers that selects subsets of features of low complexity for ensemble members, which constitutes the accurate members according to the found dependence relation. Conclusion: Experiments with six gene expression datasets demonstrate that our ensemble generating scheme based on the dependence of dataset complexity and classification error is superior to a single best classifier in the ensemble and to the traditional ensemble construction scheme that is ignorant of dataset complexity. [Copyright &y& Elsevier]

Abstract: We propose a data reduction method based on fuzzy clustering and nonnegative matrix factorisation. In contrast to different variants of data set editing typically used for data reduction, our method is completely unsupervised, i.e., it does not need class labels to eliminate examples from a data set. Thus, it is useful in exploratory data analysis when class labels of examples are unknown or unavailable in order to gain insight into structure of different groups of patterns. Also unlike many types of unsupervised clustering relating a single example (cluster centroid) to each cluster, our method associates a set of the most representative examples with each cluster. Hence, it makes cluster structure more transparent to a data analyst. [Copyright &y& Elsevier]

Abstract: The locally linear embedding (LLE) algorithm belongs to a group of manifold learning methods that not only merely reduce data dimensionality, but also attempt to discover a true low dimensional structure of the data. In this paper, we propose an incremental version of LLE and experimentally demonstrate its advantages in terms of topology preservation. Also compared to the original (batch) LLE, the incremental LLE needs to solve a much smaller optimization problem. [Copyright &y& Elsevier]