Decision support in machine vision system for monitoring of TFT-LCD glass substrates manufacturing

This study addresses classification methodology for the automatic inspection of a range of defects on the surface of glass substrates in thin film transistor liquid crystal display glass substrate manufacturing. The proposed methodology consisted of four stages: (1) feature extraction by calculating the wavelet co-occurrence signature from the substrate images, (2) handling of imbalanced dataset using the Synthetic Minority Over-sampling TEchnique (SMOTE), (3) reduction of the feature's dimension by principal component analysis, and (4) finally choosing the best classifier between three different methods: Classification And Regression Tree (CART), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). In training the SVM and MLP classifiers, the simulated annealing algorithm was used to obtain the optimal tuning parameters for the classifiers. From the industrial case study, the proposed feature extraction algorithm could remove the defect-irrelevant image features and SMOTE increased the accuracy of all three methods. Furthermore, the optimized SVM and MLP models were more accurate than the CART model whereas a higher accuracy of 89.5% was observed for the proposed SVM model.     

Toward breast cancer survivability prediction models through improving training space

Due to the difficulties of outlier and skewed data, the prediction of breast cancer survivability has presented many challenges in the field of data mining and pattern precognition, especially in medical research. To solve these problems, we have proposed a hybrid approach to generating higher quality data sets in the creation of improved breast cancer survival prediction models. This approach comprises two main steps: (1) utilization of an outlier filtering approach based on C-Support Vector Classification (C-SVC) to identify and eliminate outlier instances; and (2) application of an over-sampling approach using over-sampling with replacement to increase the number of instances in the minority class. In order to assess the capability and effectiveness of the proposed approach, several measurement methods including basic performance (e.g., accuracy, sensitivity, and specificity), Area Under the receiver operating characteristic Curve (AUC) and F-measure were utilized. Moreover, a 10-fold cross-validation method was used to reduce the bias and variance of the results of breast cancer survivability prediction models. Results have indicated that the proposed approach leads to improving the performance of breast cancer survivability prediction models by up to 28.34% due to the improved training data space.     

A dynamic over-sampling procedure based on sensitivity for multi-class problems

Classification with imbalanced datasets supposes a new challenge for researches in the framework of machine learning. This problem appears when the number of patterns that represents one of the classes of the dataset (usually the concept of interest) is much lower than in the remaining classes. Thus, the learning model must be adapted to this situation, which is very common in real applications. In this paper, a dynamic over-sampling procedure is proposed for improving the classification of imbalanced datasets with more than two classes. This procedure is incorporated into a memetic algorithm (MA) that optimizes radial basis functions neural networks (RBFNNs). To handle class imbalance, the training data are resampled in two stages. In the first stage, an over-sampling procedure is applied to the minority class to balance in part the size of the classes. Then, the MA is run and the data are over-sampled in different generations of the evolution, generating new patterns of the minimum sensitivity class (the class with the worst accuracy for the best RBFNN of the population). The methodology proposed is tested using 13 imbalanced benchmark classification datasets from well-known machine learning problems and one complex problem of microbial growth. It is compared to other neural network methods specifically designed for handling imbalanced data. These methods include different over-sampling procedures in the preprocessing stage, a threshold-moving method where the output threshold is moved toward inexpensive classes and ensembles approaches combining the models obtained with these techniques. The results show that our proposal is able to improve the sensitivity in the generalization set and obtains both a high accuracy level and a good classification level for each class.     

面向不均衡数据集的过抽样算法

合成少数类过抽样技术（SMOTE）中的噪声样本可能参与合成新样本，所以难以保证新样本的合理性。针对这个问题，结合聚类算法提出了改进算法CSMOTE。该算法抛弃了SMOTE在最近邻间线性插值的思想，使用少数类的簇心与其对应簇中的样本进行线性插值合成新样本，并且对参与合成的样本进行了筛选，降低了噪声样本参与合成的可能。在六个实际数据集上，将CSMOTE算法与四个SMOTE的改进算法以及两种欠抽样算法进行了多次的对比实验，CSMOTE算法在所有数据集上均获得了最高的AUC值。实验结果表明，CSMOTE算法具有更高的分类性能，可以有效解决数据集中样本分布不均衡的问题。     

RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

This study investigates how to alleviate the class imbalance problems for constructing unbiased classifiers when instances in one class are more than that in another. Since keeping the data distribution unchanged and expanding class boundaries after synthetic samples have been added influence the classification performance greatly, we take into account the above two factors, and propose a Random Walk Over-Sampling approach (RWO-Sampling) to balancing different class samples by creating synthetic samples through randomly walking from the real data. When some conditions are satisfied, it can be proved that, both the expected average and the standard deviation of the generated samples equal to that of the original minority class data. RWO-Sampling also expands the minority class boundary after synthetic samples have been generated. In this work, we perform a broad experimental evaluation, and experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms.     

L-SMOTE与SVM结合的不平衡数据集分类研究

针对不平衡数据集的低分类效率,基于L-SMOTE算法和混合核SVM提出了一种改进的SMOTE算法（FTL-SMOTE）。利用混合核SVM对数据集进行分类。提出了噪声样本识别三原则对噪声样本进行精确识别并予以剔除,进而利用F-SMOTE和T-SMOTE算法分别对错分和正确分类的少类样本进行采样。如此循环,直到满足终止条件,算法结束。通过在UCI数据集上与经典的SMOTE等重要采样算法以及标准SVM的大量实验表明,该方法具有更好的分类效果,改进算法与L-SMOTE算法相比,运算时间大幅减少。     

非均衡数据分类经典方法综述与面向医疗领域的实验分析

近年来,人工智能技术被广泛地应用于多个领域.其中,智慧医疗场景得到了普遍关注,并产生了大量临床辅助诊断和医疗方案推荐的实际应用.然而,由于人工智能技术的本质在于通过从大量真实数据中进行模式抽取,从而预测未知情况,因此真实数据的数据特征和数据质量将直接影响人工智能应用的效果.相比其他智能应用领域,由于罕见病患者在人群中总...     

基于遗传算法改进的少数类样本合成过采样技术的非平衡数据集分类算法

针对少数类样本合成过采样技术(SMOTE)在处理非平衡数据集分类问题时,为少数类的不同样本设置相同的采样倍率,存在一定的盲目性的问题,提出了一种基于遗传算法(GA)改进的SMOTE方法--GASMOTE.首先,为少数类的不同样本设置不同的采样倍率,并将这些采样倍率取值的组合编码为种群中的个体;然后,循环使用GA的选择、交叉、变异等算子对种群进行优化,在达到停机条件时获得采样倍率取值的最优组合;最后,根据找到的最优组合对非平衡数据集进行SMOTE采样.在10个典型的非平衡数据集上进行的实验结果表明:与SMOTE算法相比,GASMOTE在F-measure值上提高了5.9个百分点,在G-mean值上提高了1.6个百分点;与Borderline-SMOTE算法相比,GASMOTE在F-measure值上提高了3.7个百分点,在G-mean值上提高了2.3个百分点.该方法可作为一种新的解决非平衡数据集分类问题的过采样技术.     

控制系统的一种直接盲辨识方法

提出一种在时域中直接盲辨识系统传递函数的方法。首先利用过采样方法，将SISO系统转换为具有相同零极点的SIMO系统进行处理；然后通过对新的SIMO模型先估计分子参数、后估计分母参数的方法，即可获得原SISO模型的参数估计。该方法也可用于辨识非最小相位系统。     

基于堆叠分类器的心电异常监测模型设计

针对传统的人工监测心脏疾病的方法对资深医生的依赖性强,需要一定的先验知识,且其监测疾病的速度和准确性有待提高等问题,提出了一种基于堆叠分类器的心电（ECG）监测算法来用于心脏异常的判定。首先,将多种机器学习算法的优势相结合,通过叠加分类器的方式集成起来,从而弥补了单个机器学习算法学习的局限性;其次,使用合成少数过采样技术（SMOTE）对原有的数据集进行了数据扩充,使得各种疾病的数量持平从而增强数据的平衡性。通过在MIT-BIH数据集上与其他机器学习算法的结果进行比较评估,实验结果表明所提算法能够提高ECG异常监测的准确性。