Bio-medical Ontologies Maintenance and Change Management

Classifying Patterns in Bioinformatics Databases 207
Table 6. ABMMC Performance with Hold-Out Methodology
Classes Instances for Instances for Number Error
Training set Test set of Errors Rate
EI 464 303 38/1190 3.1%
IE 485 280 43/1190 3.6%
It is clear that ABMMC surpasses the outcome shown by BRAIN. Notice
that, since the third class is non-significant, the instances which fall into that
class are ignored for comparison purposes.
4.2.2 Cross-Validation
From the 3190 instances of the database, 1000 samples were randomly taken
to form a subset to which the 10-Fold Cross-Validation methodology was
applied. The table 7 shows the performance of the ABMMC compared against
BRAIN and some others Machine Learning algorithms mentioned in [35].
Table 7. ABMMC Performance with Hold-Out Methodology
Machine Learning EI IE
Algorithm Error Rate Error Rate
BRAIN 0.050 0.040
ABMMC 0.068 0.078
KBANN 0.076 0.085
BackProp 0.057 0.107
PEBLS 0.082 0.075
Perceptron 0.163 0.174
ID3 0.106 0.140
COBWEB 0.150 0.095
Near. Neighbor 0.116 0.091
The ABMMC overcomes all the algorithms except BRAIN in the classification
of the significant classes IE and EI. It is important to notice that
the BRAIN algorithm was created especially to be applied to the “Primate
splice-junction gene sequences (DNA) with associated imperfect domain theory”
database. On the other hand, the ABMMC is for general purposes; that
is, it was not made to be applied in any particular database. Hence, as a
future work, we propose the design of a similarity algorithm that helps in
specific classifications.
5 Conclusions and Future Work
With the present work, three extensions to the original model of alpha-beta
associative memories are presented. First, an extension to the original heteroassociative
memory algorithm, which enables it to correctly recall the