The time calculated is in seconds and is for one thousand instances of the dataset.
This predicted distribution is obtained by taking into account non-linear interactions between the predictive variables to evaluate the similarity between the instances of the dataset.
KSEs computed for many randomized instances of the dataset are employed to generate a null distribution and establish a (multiple testing corrected) significance threshold (p = 0.05, Figure 1).
For instance, when the dataset of 33 meiosis related genes is used, the rate of gene duplication and fixation is estimated to be 0.0013 and 0.0011 for species of the Drosophila and Sophophora subgenera, respectively (the estimate becomes 0.0009 for the Sophophora subgenus if the only likely non-functional D. willistoni mtrm-dup gene is not included in the calculations; see Table 5).
The dataset manager is a database that registers instances of the available datasets and maintains the available privacy budget.
Tree Score We define the weighted best-J-Score ( <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0002247.e004.PNG" class= inline-graphic"/> ) where J*i is the best J-Score for class i in the tree, ni is the number of instances in class i, c is the number of classes and N is the number of instances in the dataset.
The leave-one-out technique is, in essence, an n-fold cross-validation technique (n being the number of instances in the dataset) and, for a small dataset, a good predictor of a scheme's performance on an independent dataset.
For instance, with the dataset OS, imputation of missing values with kNN method gives an average CPP value (for the Ward algorithm) that equals 42.9%, while the average CPP values for all the other methods only equals 40.6% whereas its RMSE value is one of highest (see Table 4).
Then, the result is the ratio of the sum of the intra-class distances to the sum of the inter-class distances for each input example: N2 = frac{sum_{i=1}^{N}mathit{intraDist}(x_{i})}{sum_{i=1}^{N}mathit{interDist}(x_{i})} (13) where N is the total number of instances in the dataset.
The significance p-value of observing k instances assigned by the algorithm to a given category in a set of n instances is given by <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0002247.e003.PNG" class= inline-graphic"/>, where K is the total number of instances assigned to the class (the category) and N is the number of instances in the dataset.
