Case Study: Aircraft accident Analysis using different classifiers

In real world datasets, lots of redundant and conflicting data exist. The performance of a classification algorithm in data mining is greatly affected by noisy information (i.e. redundant and conflicting). These parameters not only increase the cost of mining process, but also degrade the detection performance of the classifiers. They have to be removed to increase the efficiency and the accuracy of the classifiers. Data mining is a data analysis process which is performed for large volume of data. The methodology for evaluating risk and safety issues of aircraft accidents is proposed in this work. This work focuses on different feature selection techniques applied on the dataset of an airline database to understand and clean the dataset. The following evaluators are like CFS,CS,GR, Information Gain, OneR Attribute, PCA Transformer, ReliefF Attribute and SU Attribute used in this study in order to reduce the number of initial attributes. The classification algorithms such as Decision Tree (DT), Naive Bayes (NB), Artificial Neural Network (ANN), K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) are used to predict the warning level of the component as the class attribute.