Analysis and comparison of classification algorithms
Peng Xinguang · Electronic Product Reliability and Environmental Testing · 2004
It summarizes the main features of decision tree learning algorithm and rule learning algorithm by in-depth analysis and comparison from all aspects such as prediction accuracy, learning efficiency and robustness. It is shown that RIPPER is superior to other algorithms in terms of complexity in computation, classified precision and noisy data adaptability because of its adoption of the repeated incremental reduction mechanism, and it is more suitable to the intrusion detection.