Classification of Microarrays with kNN: Comparison of Dimensionality Reduction Methods
Dimensionality reduction can often improve the performance of the k-nearest neighbor classifier (kNN) for high-dimensional data sets, such as microarrays. The effect of the choice of dimensionality reduction method on the predictive performance of kNN for
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tract. Dimensionality reduction can often improve the performance of the k-nearest neighbor classifier (kNN) for high-dimensional data sets, such as microarrays. The effect of the choice of dimensionality reduction method on the predictive performance of kNN for classifying microarray data is an open issue, and four common dimensionality reduction methods, Principal Component Analysis (PCA), Random Projection (RP), Partial Least Squares (PLS) and Information Gain(IG), are compared on eight microarray data sets. It is observed that all dimensionality reduction methods result in more accurate classifiers than what is obtained from using the raw attributes. Furthermore, it is observed that both PCA and PLS reach their best accuracies with fewer components than the other two methods, and that RP needs far more components than the others to outperform kNN on the non-reduced dataset. None of the dimensionality reduction methods can be concluded to generally outperform the others, although PLS is shown to be superior on all four binary classification tasks, but the main conclusion from the study is that the choice of dimensionality reduction method can be of major importance when classifying microarrays using kNN.
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Introduction
Microarray gene-expression technology has spread across the research community with immense speed during the last decade [1]. Being able to effectively learn from data generated through this technology is important for many reasons, including allowing for early accurate diagnoses which might lead to proper choice of treatments and therapies [2,3]. On the other hand, this type of highdimensional data, often involving thousands of attributes, creates challenges for many learning algorithms, including the well-known k-nearest neighbor classifier (kNN) [4]. H. Yin et al. (Eds.): IDEAL 2007, LNCS 4881, pp. 800–809, 2007. c Springer-Verlag Berlin Heidelberg 2007
Classification of Microarrays with kNN
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The kNN has a very simple strategy as a learner: instead of generating an explicit model, it keeps all training instances. A classification is made by measuring the distances from the test instance to all training instances, most commonly using the Euclidean distance. Finally, the majority class among the k nearest instances is assigned to the test instance. This simple form of kNN can however be both inefficient and ineffective for high-dimensional data sets due to presence of irrelevant and redundant attributes. Therefore the classification accuracy of kNN often decreases with an increase in dimensionality. One possible remedy to this problem that earlier has shown to be successful is to use dimensionality reduction [5]. The kNN has earlier been demonstrated to allow for successful classification of microarrays [2] and it has also been shown that dimensionality reduction can further improve the performance of kNN for this task [5]. However, it is an open question if the choice of dimensionality reduction technique has any impact in the performance, and for this purpose, four commonly employed dimensionalit
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