关键词： Forecasting  

摘要： Data Mining is an analytic process designed to search through a large database in search of consistent patterns and/or systematic relationships between variables among data. Decision trees are one of the models that can be applied to data mining. There is another important model used in data mining for prediction called "Naive Bayes classifier". This paper is an attempt to help prospective students to make wise career decisions using these data mining tools. A student enters his Entrance Rank, Gender (M/F), Sector (rural/urban) and Reservation (OBC/SC/ST/GEN) category. Based on the entered information the model will return which branch of study is Excellent, Good, Average or Poor for him/her based on history data analysis using data mining techniques. Also in this paper we compare the performance of decision trees and Naive Bayes classifier on the same training and test data for this problem.

关键词： 人工   神经网络   机器学习方法  

摘要： 机器学习方法经常被应用到解决医学和生物信息学的问题。本文列举了一些把机器学习方法应用到生物信息学领域的实例。比如:组建多重神经网络,运用该神经网络对4种不同形势的肿瘤患者进行分类。

关键词： genes   peroxisomal proteins   PTS1 tripeptides   EST database   novel plant peroxisomal targeting signal identification   experimental verification rate   plant peroxisomal proteome   specific targeting-enhancing pattern   Arabidopsis Proteins   Genes   In vivo   Crop (plant)   Tripeptides   targeting   Machine Learning   Proteomes   Plant Proteins   plants   Prediction methods   Peroxisomes  

摘要： In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

关键词： 数学建模   Adaboost算法   改进Haar特征   Radon变换   3次B样条   神经网络  

摘要： 首先,针对不同光照、复杂背景和投影失真的车牌图像建立基于Adaboost算法和改进Haar特征的车牌检测模型;然后,运用Radon变换进行车牌校正,并结合3次B样条小波变换和识别反馈模型对字符进行粗和精分割;最后,根据汉字和数字字母的不同结构特征,采用不同的算法提取特征,特别是针对车牌字符特点,训练汉字、字母和数字字母3种神经网络模型用于建立字符识别模型.实验结果表明该模型是实用的.

关键词： Fault diagnosis   Artificial neural network   Support vector machine  

摘要： Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Skin penetration   QSARs   QSPRs   Gaussian process regression  

摘要： Improving predictions of skin permeability is a significant problem for which mathematical solutions have been sought for around twenty years. However, the current approaches are limited by the nature of the models chosen and the nature of the dataset. This is an important problem, particularly with the increased use of transdermal and topical drug delivery systems. In this work, we apply K-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the skin permeability coefficient of penetrants. A considerable improvement, both statistically and in terms of the accuracy of predictions, over the current quantitative structure-permeability relationships (QSPRs) was found. Gaussian processes provided the most accurate predictions, when compared to experimentally generated results. It was also shown that using five molecular descriptors - molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups - can produce better predictions than when using only lipophilicity and the molecular weight, which is an approach commonly found with QSPRs. The Gaussian process regression with five compound features was shown to give the best performance in this work. Therefore, Gaussian processes would appear to provide a viable alternative to the development of predictive models for skin absorption and underpin more realistically mechanistic understandings of the physical process of the percutaneous absorption of exogenous chemicals. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Tetrahymena pyriformis toxicity   Quantitative structure-toxicity relationship   Substructure pattern recognition   Support vector machine   Machine learning   Information gain  

摘要： There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： behavior prediction   Down syndrome   Naive Bayes   Ts1Cje   Ts65Dn  

摘要： Down syndrome (DS), caused by trisomy of human chromosome 21 (HSA21), is a common genetic cause of cognitive impairment. This disorder results from the overexpression of HSA21 genes and the resulting perturbations in many molecular pathways and cellular processes. Knowledge-based identification of targets for pharmacotherapies will require defining the most critical protein abnormalities among these many perturbations. Here the authors show that using the Ts65Dn and Ts1Cje mouse models of DS, which are trisomic for 88 and 69 reference protein coding genes, respectively, a simple linear Naive Bayes classifier successfully predicts behavioral outcome (level of locomotor activity) in response to treatment with the N-methyl-D-aspartate (NMDA) receptor antagonist MK-801. Input to the Naive Bayes method were simple protein profiles generated from cortex and output was locomotor activity binned into three levels: low, medium, and high. When Feature Selection was used with the Naive Bayes method, levels of three HSA21 and two non-HSA21 protein features were identified as making the most significant contributions to activity level. Using these five features, accuracies of up to 88% in prediction of locomotor activity were achieved. These predictions depend not only on genotype-specific differences but also on within-genotype individual variation in levels of molecular and behavioral parameters. With judicious choice of pathways and components, a similar approach may be useful in analysis of more complex behaviors, including those associated with learning and memory, and may facilitate identification of novel targets for pharmacotherapeutics.

关键词： 机器学习   无师自通   未标记数据集   稀疏编码   监督学习  

摘要： 机器学习的发展趋势，就是不断地使用易获得的、廉价的数据作为样本数据，来辅助训练数据进行学习。“无师自通”学习是迁移学习的一种改进，它使用未标记的数据集作为样本数据集，且并不假设未标记数据集和标记数据集有着同样的数据分布，它运用稀疏编码方法来对未标记数据集得到一个更高层次的表示。然后，再使用经典的监督学习方法如SVM方法来对得到的新的数据集进行机器学习。该文将介绍“无师自通”学习法的主要原理，并对其核心算法一稀疏编码算法进行深入分析并通过实验给出具体的实例。

关键词： 信用风险   数据挖掘   商业银行   贷款业务   预期贷款收益  

摘要： 贷款资产是商业银行的主要资产，同时也是商业银行日常经营中的一个重要利润来源。然而，不良的放贷行为将使银行蒙受巨大的经济损失，并可能导致银行经营失败，甚至妨碍社会经济稳定。所谓“不良放贷”，是指银行向其交易对手出借货币资金使用权，而交易对手未能按照贷款协议约定履行还本付息的责任，造成银行的实际贷款收益与预期贷款收益发生偏差。在银行实际运营中，这种偏差现象是否将发生的不确定性被称为银行贷款的“信用风险”，有时也称为“违约风险”。\n 正式论文将由“导论”、“文献综述”、“数据挖掘分类技术及其优越性”、“数据挖掘分类模型的应用”和“结论”五部分组成。“第1章导论”主要讨论数据挖掘技术应用于商业银行中的研究背景、研究的意义及研究目标、以及前人所做的相关研究。“第2章文献综述”主要对信用风险评估做了回顾。“第3章数据挖掘分类技术及其优越性”讨论了数据挖掘技术的基本思想及其在应用于信用风险分类的优越性。“第4章数据挖掘分类模型的应用”是本文的重点，分析数据挖掘分类技术(包括Logistic回归、神经网络和C5.0分类树)如何应用于信用风险类别预测，并采用某银行的数据对该问题做数据实验，以验证方法的有效性和准确性，最后分析试验结果，以得到该问题的有效解决方法。\n 最后，本文搭建并规范化了一套“‘由数据仓库导出数据’到‘数据格式转换’到‘以类别为单位的数据集融合为整集’再到‘数据录入数据挖掘系统并实施挖掘任务’”的银行数据挖掘工作流程。从结果评估部分看，我们可以准确地(超过90％的准确率)为新的贷款协议申请判别其风险类别，即区分“不良”类或“良好”类。同时，我们可以向此风险分类系统的使用者清楚地展现用以判定协议类别的分类规律。\n 本文通过数据实验得到：数据挖掘分类模型对于商业银行贷款信用风险类别预测问题广泛适用，它能够完全解决传统违约度量技术对于分值排序居中的贷款数据在类别判定上模棱两可、难以辨识的问题。同时，没有一种唯一最优的数据挖掘技术适用于贷款风险分类问题，应用者需要根据数据的实际情况选择最优的方法。