Loading Now

Summary of Robust Prediction Model For Multidimensional and Unbalanced Datasets, by Pooja Thakar et al.


Robust Prediction Model for Multidimensional and Unbalanced Datasets

by Pooja Thakar, Anil Mehta, Manisha

First submitted to arxiv on: 5 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A novel Robust Prediction Model is proposed to tackle the challenges of multidimensional, imbalanced, and missing values commonly encountered in real-world datasets. This model aims to identify relevant attributes from a large pool of data, enabling novice users to leverage its predictive capabilities for informed decision-making. The model’s robustness is demonstrated through experiments on five diverse datasets spanning health, education, business, and fraud detection domains.
Low GrooveSquid.com (original content) Low Difficulty Summary
A new model helps solve big problems with real-world data. Right now, it’s hard to use data mining because of issues like too many features, not enough examples, or missing values. The model makes it easier for people who aren’t experts to find the right information from a huge dataset and make good decisions.

Keywords

» Artificial intelligence