Summary of Explainable Multi-label Classification Of Mbti Types, by Siana Kong et al.
Explainable Multi-Label Classification of MBTI Types
by Siana Kong, Marina Sokolova
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This study seeks to determine the most effective machine learning model for accurately classifying Myers-Briggs Type Indicator (MBTI) types from Reddit posts and a Kaggle dataset. The researchers utilize multi-label classification via Binary Relevance, coupled with Explainable Artificial Intelligence (XAI) methods to ensure transparency and understandability of the process and results. Glass-box learning models are employed, specifically k-Nearest Neighbour, Multinomial Naive Bayes, and Logistic Regression, which are designed for simplicity, transparency, and interpretability. The findings indicate that Multinomial Naive Bayes and k-Nearest Neighbour perform better when classes with Observer (S) traits are excluded, while Logistic Regression achieves its best results when all classes have > 550 entries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about using machine learning to figure out people’s personality types based on what they write online. The researchers want to know which type of machine learning model works the best for this task. They use a special way of doing machine learning called Binary Relevance, and also try to make it easy to understand how the model makes its decisions. They test different models, like k-Nearest Neighbour and Multinomial Naive Bayes, and find out that some work better than others in certain situations. |
Keywords
» Artificial intelligence » Classification » Logistic regression » Machine learning » Naive bayes
