Search

One-class support vector machine (OCSVM) has demonstrated superior performance in one-class classification problems. However, its training is impractical for large-scale datasets owing to high computational complexity with respect to the number of training instances. In this study, we propose an approximate training method based on the concept of expected margin to obtain a model identical to full training with reduced computational burden. The proposed method selects prospective support vectors using multiple OCSVM models trained on small bootstrap samples of an original dataset. The final OCSVM model is trained using only the selected instances. The proposed method is not only simple and straightforward but also considerably effective in improving the training efficiency of OCSVM. Preliminary experiments are conducted on large-scale benchmark datasets to examine the effectiveness of the proposed method in terms of approximation performance and computational efficiency.

A new semi-supervised support vector regression method is proposed.Label distribution is estimated by probabilistic local reconstruction algorithm.Different oversampling rate is used based on uncertainty information.Expected margin based pattern selection is used to reduce the training complexity.The proposed method improves the prediction performance with lower time complexity. Dataset size continues to increase and data are being collected from numerous applications. Because collecting labeled data is expensive and time consuming, the amount of unlabeled data is increasing. Semi-supervised learning (SSL) has been proposed to improve conventional supervised learning methods by training from both unlabeled and labeled data. In contrast to classification problems, the estimation of labels for unlabeled data presents added uncertainty for regression problems. In this paper, a semi-supervised support vector regression (SS-SVR) method based on self-training is proposed. The proposed method addresses the uncertainty of the estimated labels for unlabeled data. To measure labeling uncertainty, the label distribution of the unlabeled data is estimated with two probabilistic local reconstruction (PLR) models. Then, the training data are generated by oversampling from the unlabeled data and their estimated label distribution. The sampling rate is different based on uncertainty. Finally, expected margin-based pattern selection (EMPS) is employed to reduce training complexity. We verify the proposed method with 30 regression datasets and a real-world problem: virtual metrology (VM) in semiconductor manufacturing. The experiment results show that the proposed method improves the accuracy by 8% compared with conventional supervised SVR, and the training time for the proposed method is 20% shorter than that of the benchmark methods.

As modern-day adolescents use the Internet on both personal computer (PC) and smartphone, this study examined the phenomenon of problematic internet use by taking account of Internet usage on both PC and smartphone together, based on the theoretical framework of substitution/complementarity of media use. For this, latent profile analysis, nonlinear canonical correlation analysis, and logistic/probit regression analyses were performed on 653 Korean adolescents. Latent profile analysis identified six classes of distinct problematic internet use patterns. In brief, two latent classes showed substituting patterns, two other classes showed complementing patterns, and the last two showed neither. According to nonlinear canonical correlation analysis, classification by latent profile analysis was mainly associated with individual variables such as 'PC game,' 'instant messaging,' 'gender,' and 'decreased PC usage time.' Further, logistic/probit regression analyses revealed that male adolescents were more likely to be included in the complementation class, because they played PC games more than female adolescents. Implications and limitations of the study are discussed. Problematic internet use on both PC and smartphone were examined together.Latent profile analysis identified six classes of problematic internet use patterns.More males were included in the complementation class because of PC games.

Support Vector Machines (SVMs) are amongst the most powerful classification algorithms in machine learning and data mining. However, SVMs are limited by high training complexity when training with large datasets. Pattern selection methods have been proposed to reduce the training complexity by selecting a smaller subset of important patterns among all training patterns. In this paper, we propose a new pattern selection method called Expected Margin–based Pattern Selection (EMPS), which selects patterns based on an estimated margin for SVM classifiers. With the estimated margin, EMPS selects patterns that are likely to become support vectors located on the margin boundary and inside the margin region; however, other patterns including noise support vectors are discarded. The experimental results involving 15 benchmark datasets and one real–world semiconductor manufacturing dataset showed that EMPS exhibits excellent performance and stability.

Highlights? A pattern selection method called Expected Margin based Pattern Selection (EMPS) is proposed. ? EMPS reduces the training complexities of SVR for use as a response modeling dataset. ? The experimental results involving one real-world marketing dataset showed that EMPS improved SVR efficiency for response modeling. Two-stage response modeling, identifying respondents and then ranking them according to their expected profit, was proposed in order to increase the profit of direct marketing. For the second stage of two-stage response modeling, support vector regression (SVR) has been successfully employed due to its great generalization performances. However, the training complexities of SVR have made it difficult to apply to response modeling based on the large amount of data. In this paper, we propose a pattern selection method called Expected Margin based Pattern Selection (EMPS) to reduce the training complexities of SVR for use as a response modeling dataset with high dimensionality and high nonlinearity. EMPS estimates the expected margin for all training patterns and selects patterns which are likely to become support vectors. The experimental results involving 20 benchmark datasets and one real-world marketing dataset showed that EMPS improved SVR efficiency for response modeling.

Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.

In semiconductor manufacturing processes, run-to-run (R2R) control is used to improve productivity by adjusting process inputs run by run. A process will be controlled based on information obtained during or after a process, including metrology values of wafers. Those metrology values, however, are usually available for only a small fraction of sampled wafers. In order to overcome the limitation, one can use virtual metrology (VM) to predict metrology values of all wafers, based on sensor data from production equipments and actual metrology values of sampled wafers. In this paper, we develop VM prediction models using various data mining techniques. We also develop a VM embedded R2R control system using the exponentially weighted moving average (EWMA) scheme. The experiments consist of two parts: (1) verifying VM prediction models with actual production equipments data, and (2) conducting simulations of the R2R control system. Our VM prediction models are found to be accurate enough to be directly implemented in actual manufacturing processes. The simulation results show that the VM embedded R2R control system improves productivity.

Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafer's quality after each process can save resources, but it is unrealistic and impractical because additional measuring processes put in between each pair of contiguous processes significantly increase the total production time. Metrology, as is employed for product quality monitoring tool today, covers only a small fraction of sampled wafers. Virtual metrology (VM), on the other hand, enables to predict every wafer's metrology measurements based on production equipment data and preceding metrology results. A well established VM system, therefore, can help improve product quality and reduce production cost and cycle time. In this paper, we develop a VM system for an etching process in semiconductor manufacturing based on various data mining techniques. The experimental results show that our VM system can not only predict the metrology measurement accurately, but also detect possible faulty wafers with a reasonable confidence.

Response modeling has become a key factor to direct marketing. In general, there are two stages in response modeling. The first stage is to identify respondents from a customer database while the second stage is to estimate purchase amounts of the respondents. This paper focuses on the second stage where a regression, not a classification, problem is solved. Recently, several non-linear models based on machine learning such as support vector machines (SVM) have been applied to response modeling. However, there is a major difficulty. A typical training dataset for response modeling is so large that modeling takes very long, or, even worse, modeling may be impossible. Therefore, sampling methods have been usually employed in practice. However a sampled dataset usually leads to lower accuracy. In this paper, we employed an @e-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.