Summary of Comparative Analysis Of Xgboost and Minirocket Algortihms For Human Activity Recognition, by Celal Alagoz
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition
by Celal Alagoz
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates the performance of two machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and MiniRocket, in recognizing human activities from smartphone sensor data. The experiments use a dataset from the UCI repository, featuring accelerometer and gyroscope signals from 30 volunteers performing various activities while wearing a smartphone. Both XGBoost and MiniRocket achieve high accuracy rates, with XGBoost slightly outperforming MiniRocket. Additionally, the study highlights the computational efficiency of XGBoost and potential advantages of using raw sensor data or fusion techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special algorithms to recognize human activities from data collected by smartphones. The algorithms are tested on a dataset of 30 volunteers performing different activities while wearing a smartphone. The results show that both algorithms can accurately identify the activities, with one algorithm being slightly better than the other. This study is important because it helps us understand how well these algorithms work and how they can be used to recognize human activities in the future. |
Keywords
* Artificial intelligence * Extreme gradient boosting * Machine learning * Xgboost
