Seoung Bum Kim

One-class classification-based control charts for multivariate process monitoring

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Various control chart methods have been used in healthcare and public health surveillance to detect increases in the rates of diseases or their symptoms. Although the observations in many health surveillance applications are often discrete, few efforts have been made to ex- plore the behavior of detection methods in discrete distributions. Joner et al. (Statist. Med. 2008; 27:2555–2575) investigated and compared the performance of the scan statistic methods with the cumulative sum (CUSUM) charts under a Bernoulli distribution. In this paper we compare the performance of three detection methods: temporal scan statistic, CUSUM, and exponential weighted moving average (EWMA) when the observations follow the Poisson distribution. A simulation study showed that the Poisson CUSUM and EWMA charts generally outperformed the Poisson scan statistic methods. In comparisons between CUSUM and EWMA, the CUSUM charts were superior in dealing with a large shift with a later change in time. However, the EWMA charts outperformed the CUSUM charts in situations with a small shift and an early change in time. The methods were also compared with thyroid cancer using a real data set. Copyright

Bootstrap-Based T2 Multivariate Control Charts

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiveness of the bootstrap technique with multivariate control charts. In the present study, we propose a bootstrap-based multivariate T 2 control chart that can efficiently monitor a process when the distribution of observed data is nonnormal or unknown. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with a traditional Hotellings T 2 control chart and the kernel density estimation (KDE)-based T 2 control chart. The results showed that the proposed chart performed better than the traditional T 2 control chart and performed comparably with the KDE-based T 2 control chart. Furthermore, we present a case study to demonstrate the applicability of the proposed control chart to real situations.

A hybrid approach of goal programming for weapon systems selection

Because weapon systems are perceived as crucial in determining the outcome of a war, selecting weapon systems is a critical task for nations. Just as with other forms of decision analysis involving multiple criteria, selecting a weapon system poses complex, unstructured problems with a huge number of points that must be considered. Some defense analysts have committed themselves to developing efficient methodologies to solve weapon systems selection problems for the Republic of Koreas (ROK) Armed Forces. In the present study, we propose a hybrid approach for weapon systems selection that combines analytic hierarchy process (AHP) and principal component analysis (PCA) to determine the weights to assign to the factors that go into these selection decisions. These weights are inputted into a goal programming (GP) model to determine the best alternative among the weapon systems. The proposed hybrid approach that combines AHP, PCA and GP process components offsets the shortcomings posed by obscurity and arbitrariness in AHP and therefore can provide decision makers with more reasonable and realistic decision criteria than AHP alone. A case study on weapon system selection for the air force demonstrates the usefulness and effectiveness of the proposed hybrid AHP-PCA-GP approach.

Unsupervised feature selection using weighted principal components

Research highlights? Unsupervised features selection method is proposed. ? The proposed method can successfully detect true significant features. ? Integration of PCA and control charts techniques. Feature selection has received considerable attention in various areas as a way to select informative features and to simplify the statistical model through dimensional reduction. One of the most widely used methods for dimensional reduction includes principal component analysis (PCA). Despite its popularity, PCA suffers from a lack of interpretability of the original feature because the reduced dimensions are linear combinations of a large number of original features. Traditionally, two or three dimensional loading plots provide information to identify important original features in the first few principal component dimensions. However, the interpretation of what constitutes a loading plot is frequently subjective, particularly when large numbers of features are involved. In this study, we propose an unsupervised feature selection method that combines weighted principal components (PCs) with a thresholding algorithm. The weighted PC is obtained by the weighted sum of the first k PCs of interest. Each of the k loading values in the weighted PC reflects the contribution of each individual feature. We also propose a thresholding algorithm that identifies the significant features. Our experimental results with both the simulated and real datasets demonstrated the effectiveness of the proposed unsupervised feature selection method.

Genetic algorithm-based feature selection in high-resolution NMR spectra

High-resolution nuclear magnetic resonance (NMR) spectroscopy has provided a new means for detection and recognition of metabolic changes in biological systems in response to pathophysiological stimuli and to the intake of toxins or nutrition. To identify meaningful patterns from NMR spectra, various statistical pattern recognition methods have been applied to reduce their complexity and uncover implicit metabolic patterns. In this paper, we present a genetic algorithm (GA)-based feature selection method to determine major metabolite features to play a significant role in discrimination of samples among different conditions in high-resolution NMR spectra. In addition, an orthogonal signal filter was employed as a preprocessor of NMR spectra in order to remove any unwanted variation of the data that is unrelated to the discrimination of different conditions. The results of k-nearest neighbors and the partial least squares discriminant analysis of the experimental NMR spectra from human plasma showed the potential advantage of the features obtained from GA-based feature selection combined with an orthogonal signal filter.

Individual variation in macronutrient regulation measured by proton magnetic resonance spectroscopy of human plasma

Proton nuclear magnetic resonance ((1)H-NMR) spectroscopy of plasma provides a global metabolic profiling method that shows promise for clinical diagnostics. However, cross-sectional studies are complicated by a lack of understanding of intraindividual variation, and this limits experimental design and interpretation of data. The present study determined the diurnal variation detected by (1)H NMR spectroscopy of human plasma. Data reduction methods revealed three time-of-day metabolic patterns, which were associated with morning, afternoon, and night. Major discriminatory regions for these time-of-day patterns included the various kinds of lipid signals (-CH(2)- and -CH(2)OCOR), and the region between 3 and 4 ppm heavily overlapped with amino acids that had alpha-CH and alpha-CH(2). The phasing and duration of time-of-day patterns were variable among individuals, apparently because of individual difference in food processing/digestion and absorption and clearance of macronutrient energy sources (fat, protein, carbohydrate). The times of day that were most consistent among individuals, and therefore most useful for cross-sectional studies, were fasting morning (0830-0930), postprandial afternoon (1430-1630), and nighttime samples (0430-0530). Importantly, the integrated picture of metabolism provided by (1)H-NMR spectroscopy of plasma suggests that this approach is suitable to study complex regulatory processes, including eating patterns/eating disorders, upper gastrointestinal functions (gastric emptying, pancreatic, biliary functions), and absorption/clearance of macronutrients. Hence, (1)H-NMR spectroscopy of plasma could provide a global metabolic tolerance test to assess complex processes involved in disease, including eating disorders and the range of physiological processes causing dysregulation of energy homeostasis.

Parallel machine scheduling considering a job-splitting property

This paper focuses on the problem of scheduling jobs on parallel machines considering a job-splitting property. In this problem, it is assumed that a job can be split into a discrete number of subjobs and they are processed on parallel machines independently. A two-phase heuristic algorithm is suggested for the problem with the objective of minimizing total tardiness. In the first phase, an initial sequence is constructed by an existing heuristic method for the parallel-machine scheduling problem. In the second phase, each job is split into subjobs considering possible results of the split, and then jobs and subjobs are rescheduled on the machines using a certain method. To evaluate performance of the suggested algorithm, computational experiments are performed on randomly generated test problems. Results of the experiments show that the suggested algorithm performs better than an existing one.

An effective classification procedure for diagnosis of prostate cancer in near infrared spectra

The main purpose of this study is to develop an effective classification procedure that discriminates between normal spectra and cancerous spectra in near infrared (NIR) spectroscopic data in which the classes are highly imbalanced and overlapped. Our proposed procedure consists of several steps. First, to ensure the comparability between spectra, normalization was done by dividing each spectral point by the area of the total intensity of the spectrum. Second, clustering analysis was performed with these normalized spectra to separate the spectra that represent the normal pattern from a mixed group that contains both normal and tumor spectra. Third, we conducted two-stage classification, the first being an effort to construct a classification model with the labels obtained from the preceding clustering analysis and the second being a classification to focus on the mixed group classified from the first classification model. To increase the accuracy, the second classification model was constructed based on the selected features that capture important characteristics of the spectral data. Our proposed procedure was evaluated by its classification ability in testing samples using a leave-one-out cross validation technique, yielding acceptable classification accuracy.

Sequential random k-nearest neighbor feature selection for high-dimensional data

The study proposes the ensemble-based feature selection algorithm.The proposed algorithm is especially useful for large p and small n problems.Experiments with 20 real data demonstrated the effectiveness of the proposed algorithm. Feature selection based on an ensemble classifier has been recognized as a crucial technique for modeling high-dimensional data. Feature selection based on the random forests model, which is constructed by aggregating multiple decision tree classifiers, has been widely used. However, a lack of stability and balance in decision trees decreases the robustness of random forests. This limitation motivated us to propose a feature selection method based on newly designed nearest-neighbor ensemble classifiers. The proposed method finds significant features by using an iterative procedure. We performed experiments with 20 datasets of microarray gene expressions to examine the property of the proposed method and compared it with random forests. The results demonstrated the effectiveness and robustness of the proposed method, especially when the number of features exceeds the number of observations.