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To be able to differentiate between the reference and the sample dataset, distinct names in the "Source" column are necessary.
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Parallel analysis works by generating many random parallel datasets, with the same number of variables and cases as the sample dataset; each of these parallel datasets, which are filled with independent randomly generated data, is analyzed using principal component analysis.
where and are the estimates of the mean absolute value and the variance of the sample dataset, respectively.
Table 1 describes the sample dataset used in this study.
The validity was verified by Experiments 1 and 2. Experiment 1 The sampled dataset with 120D feature was trained, and the results of 10-fold cross-validation were analyzed.
The detailed algorithms refer to Section 2. SN, SP, and GM values of classification results obtained from 10-fold cross-validation on the unsampled and sampled datasets are illustrated in Figure 6. Figure 6 shows that the effect of 10-fold cross-validation on the sampled dataset is quite good.
To make sure the great performances of SMO are not specific to a certain partition of training and independent test datasets, we randomly divided the training (85%% of the samples) and independent test (15%% of the samples) datasets for 30 times and for each time, the training and test processes were repeated.
During the reduce phase (Lines 18 22), the partition ranges ((Rrange_i) and (Srange_i)) for each shift are calculated using the sampled datasets and broadcast to stage 2 (Lines 18 20).
The training (85%% of the samples) and independent test (15%% of the samples) datasets were randomly divided for 30 times and for each time, the training and test processes were repeated.
Figure 10 Covering ratios and number of BASs for the sampled datasets.
We built the oil and gas model using a Random Forests model with 300 bootstrap replicates or classification trees (k) and using the entire sample dataset for out-of-bag (OOB) testing with replacement.
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