计算思维及开源WEKA在人工智能教学中的应用

本文将计算思维的理念引入人工智能教学活动中,探讨了一种新的以培养学生思维为主的教学方法,并在人工智能课程中通过开源WEKA软件辅助实现此种理念,让学生在教学过程中既获得了"鱼",又学会了"渔",实践了一种全新的教学模式,为推动教学改革提供了一种可借鉴的方法。     

WEKA环境下基于模糊理论的聚类算法

因特网上的数据规模大、动态性强,通常发现的知识或规则很可能是不精确和不完备的。为了克服以上不足,引入模糊理论,通过寻找模糊相似上近似集进行合理聚类,在确定聚类数目的过程中,利用平均信息熵进行最佳聚类。同时将模糊聚类算法嵌入WEKA平台,利用WEKA中的类和可视化功能,扩充了WEKA中的聚类算法。实验表明,算法对含有噪声的、分布不规则的大数据集具有很高的精度和收敛速度。     

一种新的K均值挖掘的隐私保护算法

对隐私保护数据挖掘算法进行了深入研究和分析。总结出目前研究的缺点,提出了一种新的K均值数据挖掘的隐私保护算法,通过与已有隐私保护算法的比较,在保证不减少隐私强度的情况下,提高了挖掘的精度。     

基于WEKA的数据挖掘技术在物流系统中的应用

利用WEKA作为数据挖掘工具,通过C4.5决策树分析以及Apriori关联规则的挖掘,结合实例对物流企业的客户资料和服务项目进行分析。     

Severity Prediction of Traffic Accident Using an Artificial Neural Network

In this paper, an artificial neural network (ANN) was used to predict the injury severity of traffic accidents based on 5973 traffic accident records occurred in Abu Dhabi over a 6‐year period (from 2008 to 2013). For each accident record, 48 different attributes had been collected at the time of the accident. After data preprocessing, the data were reduced to 16 attributes and four injury severity classes. In this study, WEKA (Waikato Environment for Knowledge Analysis) data‐mining software was used to build the ANN classifier. The traffic accident data were used to build two classifiers in two different ways. The whole data set were used for training and validating the first classifier (training set), while 90% of the data were used for training the second classifier and the remaining 10% were used for testing it (testing set). The experimental results revealed that the developed ANN classifiers can predict accident severity with reasonable accuracy. The overall model prediction performance for the training and testing data were 81.6% and 74.6%, respectively. To improve the prediction accuracy of the ANN classifier, traffic accident data were split into three clusters using a k‐means algorithm. The results after clustering revealed significant improvement in the prediction accuracy of the ANN classifier, especially for the training dataset. In this work, and in order to validate the performance of the ANN model, an ordered probit model was also used as a comparative benchmark. The dependent variable (i.e. degree of injury) was transformed from ordinal to numerical (1, 2, 3, 4) for (minor, moderate, sever, death). The R tool was used to perform an ordered probit. For each accident, the ordered probit model showed how likely this accident would result in each class (minor, moderate, severe, death). The accuracy of 59.5% obtained from the ordered probit model was clearly less than the ANN accuracy value of 74.6%. Copyright © 2016 John Wiley & Sons, Ltd.     

基于WEKA的软件维护性评价模型的实验研究

为了能够对软件维护性进行评价,以类级软件维护性数据集为例进行了软件维护性实验,应用Logiscope对类的维护性做了定性评价;运用Krakatau Professional收集类各个度量的度量值;通过WEKA得到了类级度量关键属性;最后,选取决策树分类方法训练分类模型,并利用训练好的分类模型对测试集进行测试,得到基本满足要求的类级软件维护性评价模型。     

WEKA中的Id3决策树算法

ID3算法是决策树学习归纳和数据挖掘中的核心方法。本文对ID3算法及其在WEKA中的实现进行了阐述,给出了使用剪枝阈值对决策树进行先剪枝的方法,最后通过实例对该方法的有效性进行了验证。     

决策树分类的属性选择方法的研究

针对ID3算法偏向于选择取值较多的属性作为测试属性的缺点,引入OneR算法选择属性的相关子集进行分类,降低无关属性和重复属性对分类的影响。实验结果表明,与ID3算法相比优化后的方案提高了ID3算法的分类准确率,降低了分类时间,同时克服了ID3算法的取值偏置问题,优化了分类结果。