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To build classifiers based on aCGH profiles.
Building and evaluating classifiers based on these synthetic datasets can be problematic, as it has been observed that they are not necessarily representative of real world review spam.
In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification.
For training the link-based classifier, 400 samples (105 positive, 295 negative) were used for building the classifier based on the local representation of the hyperlinks discussed in the "Frontier component" section.
In order to evaluate the reproducibility of the pathway markers across different dataset, we performed cross-dataset experiments, where one dataset was used for selecting the pathway markers, and the other dataset was used for building the classifier based on the selected markers and evaluating its performance.
This illustrates that inappropriate evaluation of classifiers based on microarray data is a common problem when building models to predict cancer outcomes.
Classifiers based on genes had a very large variance, compared to the BC module-based classifier.
This chapter explores classifiers based on Bayes Decision Theory.
The proposed system outperforms four classifiers in terms of accuracy and all five classifiers based on execution time.
Strong classifier can be generated by integrating weak classifiers based on the strategies proposed in Sections 4.2 and 4.3.
According to their official website, the algorithm developed by Faception helps it categorize unique individuals into 9 different "classifiers" based on their genetic and psychological traits.
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