改进朴素贝叶斯算法在火灾预警中的应用

Application of a newly developed naive Bayes algorithm in fire alarm

为解决实际场景中传统火灾预警系统识别准确率低的问题,提出了一种新的朴素贝叶斯(NB)算法,即改进朴素贝叶斯(INB)。用基于属性权重和正交矩阵相结合的优化方法对NB加以改进。属性权重考虑了每个决策类别下各个属性的不同取值对分类性能的影响,正交矩阵弱化了属性间的线性关系,降低了属性间的关联性,使其更贴近条件属性独立性假设。利用美国国家标准与技术研究院(NIST)有关火灾研究的报告资料作为仿真数据,构建了八个不同规模的数据集,滤波、归一化处理后用于INB的训练与测试。十次十折交叉验证结果表明,当数据集中含190组样本时,INB已得到有效训练,并展示出稳F_(1)定的火灾预警能力。换句话说,INB可以在小样本下得到充分训练。选取支持向量机(SVM)、反向传播(BP)神经网络和NB进行对比研究,结果表明,新改进的INB算法的识别正确率、平均查准率、平均召回率和平均值在四种算法中最高,分别为96.1%、97.3%、97.2%和97.3%。此外,与其他算法相比,INB在具有更好性能的情况下,其训练时间更短,响应迅速。综上所述,INB具有良好且稳定的火灾预警性能,可以作为火灾预警系统的核心算法。

To address the problems of low recognition accuracy of traditional early fire warning systems in actual scenarios, a newly developed naive Bayes(NB) algorithm, namely, improved naive Bayes(INB), was proposed. An optimization method based on attribute weighting and an orthogonal matrix was used to improve the NB algorithm. Attribute weighting considers the influence of different values of each attribute on classification performance under every decision category;the orthogonal matrix weakens the linear relationship between the attributes reducing their correlations, which is more closely related to the conditional independence assumption. Data from the technology report of the National Institute of Standards and Technology(NIST) regarding fire research were used for the simulation, and eight datasets of different sizes were constructed for INB training and testing after filtering and normalization. A ten-fold cross-validation suggests that INB has been effectively trained and demonstrates the stable ability in fire alarms when the dataset contains 190 sets of samples;namely, the INB can be fully trained by using small datasets. A support vector machine(SVM), a back propagation(BP) neural network, and NB were selected for comparison. The results showed that the recognition accuracy, average precision, average recall, and average measure of INB were 96.1%, 97.3%, 97.2%, and 97.3%, respectively, which is the highest among the four different algorithms. Additionally, INB has a better performance compared to NB, SVM, and BP neural networks when the training time is short. In conclusion, INB can be used as a core algorithm for fire alarm systems with excellent and stable fire alarm capabilities.