Classification of epilepsy using computational intelligence techniques

This paper deals with a real-life application of epilepsy classification, where three phases of absence seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. Artificial neural network （ANN） and support vector machines （SVMs） combined with su- pervised learning algorithms, and k-means clustering （k-MC） combined with unsupervised techniques are employed to classify the three seizure phases. Different techniques to combine binary SVMs, namely One Vs One （OvO）, One Vs All （OVA） and Binary Decision Tree （BDT）, are employed for multiclass classification. Comparisons are performed with two traditional classification methods, namely, k-Nearest Neighbour （k- NN） and Naive Bayes classifier. It is concluded that SVM-based classifiers outperform the traditional ones in terms of recognition accuracy and robustness property when the original clinical data is distorted with noise. Furthermore, SVM-based classifier with OvO provides the highest recognition accuracy, whereas ANN-based classifier overtakes by demonstrating maximum accuracy in the presence of noise.

基于人员行为分类的用能有效性评估方法

为了定量评估公共建筑的节能潜力,提出基于人员行为分类的用能有效性评估方法.该方法首先采用局部加权朴素贝叶斯(locally weighted naive Bayes,简称LWNB)算法对用电行为进行分类,在此基础上引入权重因子,构建了一种用能有效性评估模型.研究过程中通过智能插座采集不同人员办公设备一周的能耗数据,同时采用计步器与监控软件监测人员行为,利用有监督机器学习方式对人员行为进行分类.基于用能有效性评估方法,给出了不同人员的用能有效性评估值.实验表明,该行为分类方法具有较高准确率,有效性评估模型能够实现人员用能合理性的定量评估,有助于公用建筑节能潜力的精细化评估,为优化用能行为及用能设备的控制提供了参考依据.

基于K-近邻法的局部加权朴素贝叶斯分类算法

分类算法一直以来都是数据挖掘领域的研究重点,朴素贝叶斯分类算法是众多优秀分类算法之一,但由于其条件属性必需独立,使得该算法也存在着一定的局限性。为了从另外一种角度来改进该算法,提高分类性能,提出了一种基于K-近邻法的局部加权朴素贝叶斯分类算法。使用K-近邻法对属性加权,找到最合适的加权值,运用加权后的朴素贝叶斯分类算法去分类,实验表明该算法提高了分类的可靠性与准确率。