A Comparison of MLA Techniques for Classification of Network Bandwidth Loss

This paper compares the performance and accuracy of four machine learning algorithms in classifying four characteristic phenomena in wireless signal use: decreases in bandwidth due to signal over-saturation, signal improvement due to a device moving closer to a wireless signal, signal attenuation due to increasing distance, and congestion caused by competition with high intensity cross-traffic on a switch. With large enough data samples, an SVM with a moderately high C parameter yielded the smallest 95% confidence intervals when compared to the other machine learning algorithms. This indicates that machine learning techniques can be used for such applications as congestion-control.