In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best technique with the highest classification accuracy (CA). A
detailed comparison of the supervised and unsupervised algorithms in terms of
the computational time and classification accuracy is performed. The classified
occupancy status is further utilized to evaluate the probability of secondary
user outage for the future time slots, which can be used by system designers to
define spectrum allocation and spectrum sharing policies. Numerical results
show that SVM is the best algorithm among all the supervised and unsupervised
classifiers. Based on this, we proposed a new SVM algorithm by combining it
with fire fly algorithm (FFA), which is shown to outperform all other
algorithms.

利用机器学习技术，分析了频谱占用情况，比较了有监督和无监督算法的分类精度和计算时间，并利用分类结果评估了未来时间段的次级用户中断概率，在此基础上提出了一种结合萤火虫算法的支持向量机模型，其表现优于其他算法。