Bio-medical Ontologies Maintenance and Change Management

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Table 3. Algorithms Comparison
Learning J. Ortega Baffes & Mooney Alpha-Beta
Set [33] [34] Multimemories
85/106 92.5% 93% 98%
80/106 N/D N/D 98%
70/106 N/D N/D 96%
out of the 106 available(the rows labeled ”80/106” and ”70/106”). For each
case, the procedure was repeated 100 times, taking into account the 20 best
results, and calculating the maximum, minimum, and average percentage.
The results are shown in table 4.
Table 4. Algorithms Comparison
ABMMC 85 training set 80 training set 70 training set
Performance
Maximum 98% 98% 96%
Minimum 97% 96% 93%
Average 97.45% 96.7% 94.55%
4.2 DNA Splice-Junction Sequences Classification
Our results are compared with R. Rampone [35] who presented an algorithm
named BRAIN (Batch Relevance-based Artificial Intelligence). Two methodologies
are used to compare the algorithms results: Hold-Out and 10-Fold
Cross-Validation.
4.2.1 Hold-Out
The table 5 shows the result obtained in the experimental phase of the BRAIN
algorithm where 2000 instances were taken for the training set and 1186 were
left in the test set, leaving 4 repeated instances out of the experiment.
The ABMMC algorithm is assessed under the following conditions: 2000
instances were taken randomly to create the training set and the remaining
1190 were left in the test set. The procedure was repeated 20 times and the
best result was chosen. In this case, the repetitions eliminated by Rampone
are taken into account.
Table 5. BRAIN Performance with Hold-Out Methodology
Classes Instances for Instances for Number Error
Training set Test set of Errors Rate
EI 464 303 41/1186 3.4%
IE 485 280 59/1186 4.9%