Classification of the Chess Endgame problem using Logistic Regression, Decision Trees, and Neural Networks
This work addresses chess endgame classification for AI or game analysis enthusiasts, but it is incremental as it applies standard machine learning methods without introducing new algorithmic advancements.
The study tackled the Chess Endgame classification problem by comparing logistic regression, decision trees, and neural networks, with neural networks achieving the highest accuracy of 85% and decision trees at 79%. It also explored using Microsoft Azure Machine Learning for visual programming and developed a dataset visualization application in the Ring programming language.
In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also developed an application for dataset visualization using a new programming language called Ring, our experiments demonstrates that this language have simple design like Python while integrates RAD tools like Visual Basic which is good for GUI development in the open-source world