Spam detection using Random Boost

This paper proposes two alternative methods of random projections and compares their performance for robust and efficient spam detection when trained using a small number of examples. Robustness refers to learning and adaptation leading to a high level of performance despite data variability, while efficiency is concerned with (i) the complexity of the detection method employed; and (ii) the amount of training resources used for training and retraining. The first method, Random Project, employs a random projection matrix to produce linear combinations of input features, while the second method, Random Boost, employs random feature selection to enhance the performance of the Logit Boost algorithm. Random Boost is, in fact, a combination of Logit Boost and Random Forest. Experimental results, using TREC and CEAS as challenging spam benchmark sets, show that the Random Boost method significantly improves the performance of the spam filter compared to the Logit Boost algorithm (e.g., a 5% increase in AUC, which is the area under the Receiver Operating Characteristic curve), and yields similar classification accuracy compared to the Random Forest method but using only one fourth the runtime complexity of the Random Forest algorithm. Additionally, the Random Boost algorithm also reduces training time by two orders of magnitude compared to Logit Boost, which becomes important during retraining on the ever changing data streams, including adapting to adversarial tactics and “noise” injected by spammers.

► Greedy learning algorithms, such as Logit Boost, are sensitive to noise in the data.
► Random Forest is robust against noise but it is expensive for message classification.
► Random Boost is a combination of Logit Boost and Random Forest.
► Random Boost is superior to Logit Boost in accuracy and less expensive to train.
► Random Boost has similar performance to Random Forest, but it is more efficient.