Results completed with the use of synthetic datasets affected by different levels of noise and real-world datasets show the effectiveness of the algorithm, which can significantly reduce the number of false positives, false negatives, and the number of records selected for review.
This paper evaluates eight different concept drift detectors (ddm, eddm, pht, stepd, dof, adwin, Paired Learners, and ecdd) and performs tests using artificial datasets affected by abrupt and gradual concept drifts, with several rates of drift, with and without noise and irrelevant attributes, and also using real-world datasets.
A notorious feature of gene expression datasets affecting the performance of cluster analysis is high data dimensionality whereby the expression of many genes is assayed over a small number of experimental conditions (time points).
Despite the value of performing data normalization on datasets affected by within-plate effects, normalization methods often introduce bias when applied on any dataset (Dragiev et al., 2011).
When coping with datasets affected by HGT, the most reliable way to avoid artefacts is to carry out independent analyses for each marker.
The extent to which random errors in large, longitudinal datasets affect overall health and demographic profiles has important implications for the role of DSSs as platforms for public health research and clinical trials.
It is therefore unsurprising that it appears to be less useful than SERE for the detection of under- and overdispersion in the comparison of replicate datasets affected by Poisson variation.
This could be an indication that many SNPs (i.e. several hundred) in our panel are significantly associated with ApoB level or, alternatively, that a previously undetected stratification in the dataset affects the results.
Changing the training dataset affect the data distribution and therefore the parameters of the transfer function.
