Robust Prediction Model for Multidimensional and Unbalanced Datasets

TL;DR

This paper introduces a robust prediction model designed to handle multidimensional, unbalanced, and incomplete real-world datasets, aiding users in extracting meaningful patterns across various domains.

Contribution

The paper presents a novel prediction model that addresses multidimensionality, unbalance, and missing data issues, improving pattern discovery for informed decision-making.

Findings

Model performs robustly across diverse datasets

Effective in handling unbalanced and multidimensional data

Applicable in health, education, business, and fraud detection domains

Abstract

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.