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When applied to supervised classification problems, dataset complexity determines how difficult a given dataset to classify. Since complexity is a nontrivial issue, it is typically defined by a number of measures. In this paper, we explore complexity of three gene expression datasets used for two-class cancer classification. We demonstrate that estimating the dataset complexity before performing actual classification may provide a hint whether to apply a single best nearest neighbour classifier or an ensemble of nearest neighbour classifiers. [ABSTRACT FROM AUTHOR]

Random forest is a collection (ensemble) of decision trees. It is a popular ensemble technique in pattern recognition. In this article, we apply random forest for cancer classification based on gene expression and address two issues that have been so far overlooked in other works. First, we demonstrate on two different real-world datasets that the performance of random forest is strongly influenced by dataset complexity. When estimated before running random forest, this complexity can serve as a useful performance indicator and it can explain a difference in performance on different datasets. Second, we show that one should rely with caution on feature importance used to rank genes: two forests, generated with the different number of features per node split, may have very similar classification errors on the same dataset, but the respective lists of genes ranked according to feature importance can be weakly correlated. [ABSTRACT FROM AUTHOR]

A number of manifold learning algorithms have been recently proposed, including locally linear embedding (LLE). These algorithms not only merely reduce data dimensionality, but also attempt to discover a true low dimensional structure of the data. The common feature of the most of these algorithms is that they operate in a batch or offline mode. Hence, when new data arrive, one needs to rerun these algorithms with the old data augmented by the new data. A solution for this problem is to make a certain algorithm online or incremental so that sequentially coming data will not cause time consuming recalculations. 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. [ABSTRACT FROM AUTHOR]

In this paper, dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linear dimensionality reduction methods. Here we explore nonnegative matrix factorization in combination with a classifier for protein fold recognition. Since typically matrix factorization is iteratively done, convergence can be slow. To alleviate this problem, a significantly faster (more than 11 times) algorithm is proposed. [ABSTRACT FROM AUTHOR]