Call of Papers for Current Volume **************** OnLine Submission of Paper

Title: relationship between feature selection and classifier accuracy using weka for bankruptcy prediction

_____________________________________________________________________________

Title:
relationship between feature selection and classifier accuracy using weka for bankruptcy prediction
Author Name:
kiranpreet kaur,amandeep kaur
Abstract:
The Data Mining is a technique to drill database for giving meaning to the approachable data. It involves systematic analysis of large data sets. Numerous studies on bankruptcy prediction have widely applied data mining techniques to find out the useful knowledge automatically from financial databases, while few studies have proposed qualitative data mining approaches capable of eliciting and representing experts’ problem-solving knowledge from experts’ qualitative decisions. This approach uses qualitative risk factors. By using these factors qualitative prediction rules are generated using machine learning algorithm and the influence of these factors in bankruptcy is also analyzed. This research is focused on tree modeling of data that helps to make predictions about new data and use of rule based classifier. They also indicate that considerable agreement is achieved between the decision trees method and experts’ problem solving knowledge. This means that the proposed method is a suitable tool for eliciting and representing experts’ decision rules and thus it provides effective decision supports for solving bankruptcy prediction problems. Also we have seen the relationship between classifiers and feature selection .Feature selection criteria helps in improving the accuracy of the classifier .The core concept behind the topic is to get knowledge with areas of research by explore more about data, information, knowledge, data mining techniques, and tools. All the results with experiment on WEKA are finally examined.
Back