基于ELM的改进CART决策树回归算法

为提高CART(ClassificationAndRegressionTree)决策树回归算法的准确性,提出一种基于ELM(Extreme Learning Machine)的改进CART决策树回归算法——ELM-CART算法.所提算法主要是在CART回归树创建过程中,在每个叶节点使用极限学习机建模,可以得到真正意义上...     

基于Adaboost和CART结合的优化分类算法

提出了一种基于Adaboost算法和CART算法结合的分类算法。以特征为节点生成CART二叉树,用CART二叉树代替传统Adaboost算法中的弱分类器,再由这些弱分类器生成强分类器。将强分类器对数字样本和人脸样本分类,与传统Adaboost算法相比,该方法的错误率分别减少20%和86.5%。将分类器应用于目标检测上,实现了对这两种目标的快速检测和定位。结果表明,改进算法既减小了对样本分类的错误率,又保持了传统Adboost算法对目标检测的快速性。     

语调短语预测中的特征模板自动生成

在语音合成系统中,语调短语的自动预测是影响合成语音的自然度和可懂度的关键因素之一。采用了最大熵（Maximum Entropy,ME）模型从无限制的文本中预测语调短语,并且提出了一个自动生成特征模板的层次聚类算法,从而减少了最大熵模型训练过程中的人工参与。实验结果表明,对于语调短语预测而言,最大熵模型明显优于分类与回归树（Classification And Regression Trees,CART）。相比手工总结的特征模板,自动生成的特征模板不仅将语调短语预测的F-score提高了3.18%,而且将最大熵模型的大小缩小了78.38%。     

基于AP聚类与随机森林的客户流失预测研究

利率市场化、大数据迅速发展,银行业均表现出明显的"二八定律"现象,20％的优质客户占据了银行的大部分资产.那么,如何防止银行客户流失,尤其是优质客户的流失,已经成为银行越来越关注的问题.因此,建立优质客户流失预警模型就显得尤为重要.以某商业银行为例,重新对客户流失进行定义,重点关注银行优质客户的流失预警,首先使用AP聚...     

基于集成回归决策树的lncRNA-疾病关联预测方法

长链非编码RNA (long non-coding RNA,lncRNA)在各种人类复杂疾病中起着重要作用.采用计算方法推断ln cRNA-疾病间的潜在关联关系不仅有助于理解疾病的致病机理,还有助于疾病诊断、预防和治疗.文中提出了一种基于集成回归决策树的lncRNA-疾病关联预测方法.首先,利用已知的lncRNA-疾病...     

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approaches.     

基于互补模型的汉语重音检测

针对现有汉语重音检测方法正确率较低的问题,利用声学、词典和语法相关特征的不同分类器组合,基于Boosting分类回归树+条件随机场的互补模型,提出一种改进的汉语重音检测方法.在ASCCD语料库上的实验结果表明,该方法能获得84.9％的重音检测正确率,相比基于神经网络+决策树的基线系统提高2.7％.     

一种选择性SER-BagBoosting Trees集成学习研究

集成学习已成为机器学习研究的一大热点.提出了一种综合Bagging和Boosting技术特点,以分类回归树为基学习器构造一种新的相似度指标用于聚类并利用聚类技术和贪婪算法进行选择性集成学习的算法--SER-BagBoosting Trees算法.算法主要应用于回归问题.实验表明,该算法往往比其它算法具有更好的泛化性能和更高的运行效率.     

混合动力电动汽车中利用决策树CART算法的能源管理方案

针对混合动力电动汽车(HEV)氮氧化物( )排放的问题,提出了一种基于决策树CART算法的柴油混合动力能源管理策略。首先,提出了一种结合决策树与回归树的分类算法(Classification and Regression Tress,CART),针对类别和变量特征,从一个或多个预测变量中预测出个例的趋势变化关系;然后,通过控制发动机和电动机之间的扭矩分配,引入了额外的自由度以调整从纯燃料经济性情况到纯 限制情况的优化权衡;最后,采用基于软件在环路和硬件在环仿真的方法,从而根据动力系统配置了解系统性能,并调整所提出的能源管理策略。实验结果表明,提出的柴油混合动力能源管理策略中, 的减少对燃料消耗的影响,且可以通过选择最佳工作点和限制发动机动力来限制 排放的潜力。相比其他几种较新的同类方案,提出的方案在同等燃料消耗的情况下 排放量更小,在燃料消耗略有下降的情况下,可以显着降低 。     

Unknown Attack Detection: Combining Relabeling and Hybrid Intrusion Detection

Detection of unknown attacks like a zero-day attack is a research field that has long been studied. Recently, advances in Machine Learning (ML) and Artificial Intelligence (AI) have led to the emergence of many kinds of attack-generation tools developed using these technologies to evade detection skillfully. Anomaly detection and misuse detection are the most commonly used techniques for detecting intrusion by unknown attacks. Although anomaly detection is adequate for detecting unknown attacks, its disadvantage is the possibility of high false alarms. Misuse detection has low false alarms; its limitation is that it can detect only known attacks. To overcome such limitations, many researchers have proposed a hybrid intrusion detection that integrates these two detection techniques. This method can overcome the limitations of conventional methods and works better in detecting unknown attacks. However, this method does not accurately classify attacks like similar to normal or known attacks. Therefore, we proposed a hybrid intrusion detection to detect unknown attacks similar to normal and known attacks. In anomaly detection, the model was designed to perform normal detection using Fuzzy c-means (FCM) and identify attacks hidden in normal predicted data using relabeling. In misuse detection, the model was designed to detect previously known attacks using Classification and Regression Trees (CART) and apply Isolation Forest (iForest) to classify unknown attacks hidden in known attacks. As an experiment result, the application of relabeling improved attack detection accuracy in anomaly detection by approximately 11% and enhanced the performance of unknown attack detection in misuse detection by approximately 10%.