We’re happy to share LinearBoost, our latest development in machine learning classification algorithms. LinearBoost is based on boosting a linear classifier to significantly enhance performance. Our testing shows it outperforms traditional GBDT algorithms in terms of accuracy and response time across five well-known datasets.
The key to LinearBoost’s enhanced performance lies in its approach at each estimator stage. Unlike decision trees used in GBDTs, which select features sequentially, LinearBoost utilizes a linear classifier as its building block, considering all available features simultaneously. This comprehensive feature integration allows for more robust decision-making processes at every step. (View Highlight)
We believe LinearBoost can be a valuable tool for both academic research and real-world applications. Check out our results and code in our GitHub repo: https://github.com/LinearBoost/linearboost-classifier . The algorithm is in its infancy and has certain limitations as reported in the GitHub repo, but we are working on them in future plans. (View Highlight)
