Summary of Automated Classification Of Dry Bean Varieties Using Xgboost and Svm Models, by Ramtin Ardeshirifar
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models
by Ramtin Ardeshirifar
First submitted to arxiv on: 2 Aug 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 This paper presents a comparative study on classifying seven varieties of dry beans using machine learning models. The study utilizes a dataset of 12,909 samples, reduced from 13,611 through outlier removal and feature extraction. Principal Component Analysis (PCA) is applied for dimensionality reduction, followed by training two multiclass classifiers: XGBoost and Support Vector Machine (SVM). Evaluation uses nested cross-validation to assess performance and tune hyperparameters. The models achieve correct classification rates of 94.00% and 94.39%, respectively. The study demonstrates the effectiveness of machine learning approaches in agricultural applications, enhancing seed classification uniformity and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different ways to automatically sort seven types of dry beans using computer programs. It uses a big dataset of 12,909 samples to test two types of models: XGBoost and Support Vector Machine (SVM). The results show that these models can correctly identify the bean type about 94% of the time. This study is important because it helps farmers use technology to improve seed quality control and crop yield optimization. |
Keywords
» Artificial intelligence » Classification » Dimensionality reduction » Feature extraction » Machine learning » Optimization » Pca » Principal component analysis » Support vector machine » Xgboost
