Handling missing data is widely studied to make proper replacement and reduce uncertainty of data. Several approaches have been proposed for providing the most possible results. However, few studies provide solutions to the problem of missing data in extended possibility-based fuzzy relational (EPFR) databases. This type of problem in the context of EPFR databases is difficult to resolve because of the complexity of the data involved. In this paper, we propose an approach of filling missing data and query processing of the databases. To obtain the rational predict of the missing data, we adopt a concept and measurement of proximate equality of tuples to define data operation and fuzzy functional dependency (FFD). We provide a method to predict the missing data and replace the data based on our proposal. The results of the missing value process preserve those FFDs that hold in the original database instance.
Database classification suffers from two common problems, i.e., the high dimensionality and nonstationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a Support Vector Machine (SVM), and Genetic Algorithms to construct a decision-making system for data classification in various database applications. The model is mainly based on the concept that the historic database can be transformed into a smaller case-base together with a group of SVM models. As a result, the model can more accurately respond to the current data under classifying from the inductions by these SVM models generated from these smaller case bases. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.
The prediction of bank failures is an important academic topic of which many have used artificial intelligence methods to build an early warning system for this purpose. The objective of this study is to enhance the accuracy in predicting bank failures by proposing two hybrid models. In this research, two hybrid models are developed by integrating a K-means cluster method to integrate K-means with a Back-Propagation Neural Network (BPN) and a Support Vector Machines (SVM) technique for financial data classification. Datasets from the website of Federal Reserve Bank of Chicago are employed for benchmark test. Initially a K-mean clustering method is applied to preprocess the dataset thus a more homogeneous data within each cluster will be attainted. A clustering method is employed to separate the case library into smaller clusters; and lastly, a Back-Propagation Neural Network (BPN) and a Support Vector Machine (SVM) model are established and prediction results are being generated. The average forecasting accuracy for bank failures of Kmeans-BPN model is 92.79% and Kmeans-SVM model is 92.43%. In comparison to other methods, the proposed model outperforms other prediction models as the prediction accuracy of bank failures are being enhanced while it simultaneously produces valuable information for business owners and investors.
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