The Design Of Bank Device Failures Alarm System Based On Machine Learning

The normal operation of bank equipment is very important to the bank business. And the bank operation system with centralized architecture is an important part of these IT equipment monitoring systems to guarantee the normal operation of the whole bank system. Currently, banks do not make full use of the performance data and alarm data produced by the operation systems. The alarms are usually captured not predicted. In this article, we can do data mining through machine learning algorithms and then predict alarms. In this way, the banking business can be ensured to the max extend. The main research results are as follows:Based on the analysis and research of heuristics feature selection algorithms and correlation based feature selection algorithms, this paper proposed a feature selection method called BDS+FCA combing bidirectional search (BDS) and feature correlation. The algorithm improves the shortcomings of BDS’s feature redundancy and CFS’s search inefficient. On the detailed study of the features of the bank data and certain kinds of machine learning algorithms, we choose Naive Bayes and Support Vector Machines and Expectation Maximization algorithms and apply them in the system and get good effects in return. On the basis of theoretical research, we complete the detailed system design with the demand analysis. In the system, we apply the BDS+FCA algorithm and combined classification algorithm to the core module. At last, we complete the implementation of the system through the J2EE, MongoDB and Weka technology. After that, we do the system test including functional test and model effectiveness test.The test results show that the BDS+FCA algorithm can improve the time performance and classification accuracy by exclude the redundant feature and the combined classification algorithm as the core engine can do good classification predication of the alarm system in the cases of low warning data proportion. Thus we verify the effectiveness and feasibility of the BDS+FCA and Combined Classification algorithms.