Biomedical subjects

Cor J Veenman

Publications and source records attributed to Cor J Veenman.

3 recordsLinked to original sources

PubMed2005

A protocol for building and evaluating predictors of disease state based on microarray data.

MOTIVATION: Microarray gene expression data are increasingly employed to identify sets of marker genes that accurately predict disease development and outcome in cancer. Many computational approaches have been proposed to construct such predictors. However, there is, as yet, no objective way to evaluate whether a new approach truly improves on the current state of the art. In addition no 'standard' computational approach has emerged which enables robust outcome prediction. RESULTS: An important contribution of this work is the description of a principled training and validation protocol, which allows objective evaluation of the complete methodology for constructing a predictor. We review the possible choices of computational approaches, with specific emphasis on predictor choice and reporter selection strategies. Employing this training-validation protocol, we evaluated different reporter selection strategies and predictors on six gene expression datasets of varying degrees of difficulty. We demonstrate that simple reporter selection strategies (forward filtering and shrunken centroids) work surprisingly well and outperform partial least squares in four of the six datasets. Similarly, simple predictors, such as the nearest mean classifier, outperform more complex classifiers. Our training-validation protocol provides a robust methodology to evaluate the performance of new computational approaches and to objectively compare outcome predictions on different datasets.

Algorithms↗

PubMed2005

LESS: a model-based classifier for sparse subspaces.

In this paper, we specifically focus on high-dimensional data sets for which the number of dimensions is an order of magnitude higher than the number of objects. From a classifier design standpoint, such small sample size problems have some interesting challenges. The first challenge is to find, from all hyperplanes that separate the classes, a separating hyperplane which generalizes well for future data. A second important task is to determine which features are required to distinguish the classes. To attack these problems, we propose the LESS (Lowest Error in a Sparse Subspace) classifier that efficiently finds linear discriminants in a sparse subspace. In contrast with most classifiers for high-dimensional data sets, the LESS classifier incorporates a (simple) data model. Further, by means of a regularization parameter, the classifier establishes a suitable trade-off between subspace sparseness and classification accuracy. In the experiments, we show how LESS performs on several high-dimensional data sets and compare its performance to related state-of-the-art classifiers like, among others, linear ridge regression with the LASSO and the Support Vector Machine. It turns out that LESS performs competitively while using fewer dimensions.

Algorithms↗

PubMed2005

The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier.

We present the Nearest Subclass Classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the Maximum Variance Cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting. With a low variance constraint value, the classifier turns into the nearest neighbor classifier and, with a high variance parameter, it becomes the nearest mean classifier with the respective properties. In other words, the number of prototypes ranges from the whole training set to only one per class. In the experiments, we compared the NSC with regard to its performance and data set compression ratio to several other prototype-based methods. On several data sets, the NSC performed similarly to the k-nearest neighbor classifier, which is a well-established classifier in many domains. Also concerning storage requirements and classification speed, the NSC has favorable properties, so it gives a good compromise between classification performance and efficiency.

Algorithms↗