Robust Prediction Model for Multidimensional and Unbalanced Datasets
This addresses the difficulty for novice users in applying data mining to complex datasets, though it appears incremental as it builds on existing methods for attribute selection and data preprocessing.
The paper tackled the problem of using real-world data with multidimensionality, imbalance, and missing values for prediction by developing a Robust Prediction Model that selects relevant attributes and resolves these issues, demonstrating robust behavior across five datasets in health, education, business, and fraud detection domains.
Data Mining is a promising field and is applied in multiple domains for its predictive capabilities. Data in the real world cannot be readily used for data mining as it suffers from the problems of multidimensionality, unbalance and missing values. It is difficult to use its predictive capabilities by novice users. It is difficult for a beginner to find the relevant set of attributes from a large pool of data available. The paper presents a Robust Prediction Model that finds a relevant set of attributes; resolves the problems of unbalanced and multidimensional real-life datasets and helps in finding patterns for informed decision making. Model is tested upon five different datasets in the domain of Health Sector, Education, Business and Fraud Detection. The results showcase the robust behaviour of the model and its applicability in various domains.