  This paper aims at improving the classification accuracy of a Support Vector
Machine (SVM) classifier with Sequential Minimal Optimization (SMO) training
algorithm in order to properly classify failure and normal instances from oil
and gas equipment data. Recent applications of failure analysis have made use
of the SVM technique without implementing SMO training algorithm, while in our
study we show that the proposed solution can perform much better when using the
SMO training algorithm. Furthermore, we implement the ensemble approach, which
is a hybrid rule based and neural network classifier to improve the performance
of the SVM classifier (with SMO training algorithm). The optimization study is
as a result of the underperformance of the classifier when dealing with
imbalanced dataset. The selected best performing classifiers are combined
together with SVM classifier (with SMO training algorithm) by using the
stacking ensemble method which is to create an efficient ensemble predictive
model that can handle the issue of imbalanced data. The classification
performance of this predictive model is considerably better than the SVM with
and without SMO training algorithm and many other conventional classifiers.
