Quadratic Classifier from Discriminant Analysis for Classification of Multiple Attributes Data (Case Study: Fertility Data Set)

Classification is a process to group data, based on characteristics into related class. There are many methods in classification and the appropriate method is chosen based on nature of data. This paper focuses on classification of multiple attributes data using discriminant analysis. The research uses Fertility Data Set from UCI Machine Learning Repository with ten attributes of data. The experiment uses several methods of classification to find out the best result of performance. The result shows Quadratic Classifier from discriminant analysis has the best performance of classification around ninety-eight percent with the lowest errors. In summary, the appropriate method produces a good performance of classification and the quadratic classifier from discriminant analysis shows the best performance in multiple attributes data classification.