Classifying Intelligence Tests Patterns Using Machine Learning Methods

Georgios Liapis, Loukritira Stefanou, Ioannis Vlahavas

2023

Abstract

Intelligence testing assesses a variety of cognitive abilities and is frequently used in the evaluation of people for jobs, army recruitment, scholarships, and the educational system in general. Licensed psychologists and researchers create and analyze intelligence tests, setting the difficulty layer, grading them, and weighing the results on a global scale. However, developing new model tests is a time-consuming and challenging process. In this study, we lay the groundwork for developing a model that classifies the IQ patterns, in order to generate new IQ Raven tests. More specifically, we analyze Raven’s Progressive Matrices Tests, a nonverbal multiple-choice intelligence test, and their patterns using a variety of Machine Learning (ML) techniques. In such intelligence tests, the question’s data includes mostly abstract images aligned in a grid system, with one missing element and a pattern that connects them by threes in horizontal and vertical order. These tests have been labeled based on several factors, such as the number of images, the type of pattern (e.g. counting, adding, or rotating), or their complexity and in order to classify them, various ML methods are used. Results of the current study act as a defining basis for the use of advanced Neural Network models, not only for classification but also for the generation of new IQ patterns.

Paper Citation

in Harvard Style

Liapis G., Stefanou L. and Vlahavas I. (2023). Classifying Intelligence Tests Patterns Using Machine Learning Methods. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 717-724. DOI: 10.5220/0011606500003411

in Bibtex Style

@conference{icpram23,
author={Georgios Liapis and Loukritira Stefanou and Ioannis Vlahavas},
title={Classifying Intelligence Tests Patterns Using Machine Learning Methods},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={717-724},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011606500003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Classifying Intelligence Tests Patterns Using Machine Learning Methods
SN - 978-989-758-626-2
AU - Liapis G.
AU - Stefanou L.
AU - Vlahavas I.
PY - 2023
SP - 717
EP - 724
DO - 10.5220/0011606500003411