A NOVEL SPLIT SELECTION OF A LOGISTIC REGRESSION TREE FOR THE CLASSIFICATION OF DATA WITH HETEROGENEOUS SUBGROUPS

A logistic regression tree (LRT) is a hybrid machine learning method that combines a decision tree model and logistic regression models. An LRT recursively partitions the input data space through splitting and learns multiple logistic regression models optimized for each subpopulation. The split selection is a critical procedure for improving the predictive performance of the LRT. In this paper, we present a novel separability-based split selection method for the construction of an LRT. The separability measure, defined on the feature space of logistic regression models, evaluates the performance of potential child models without fitting, and the optimal split is selected based on the results. Heterogeneous subgroups that have different class-separating patterns can be identified in the split process when they exist in the data. In addition, we compare the performance of our proposed method with the benchmark algorithms through experiments on both synthetic and real-world datasets. The experimental results indicate the effectiveness and generality of our proposed method

Effect of dimension reduction on prediction performance of multivariate nonlinear time series

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Supervised learning-based collaborative filtering using market basket data for the cold-start problem

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A New Control Chart for Monitoring Reliability Using Sudden Death Testing Under Weibull Distribution

In this paper, a new control chart using sudden death testing is designed by assuming that the lifetime/failure time of the product follows the Weibull distribution. The structure of the proposed chart is presented. The control chart coefficient is determined using some specified average run length for the in control process and the shifted process. Simulation study is given for the illustration purpose.11Ysciescopu

Sampling plans based on truncated life test for a generalized inverted exponential distribution

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An Attribute Control Chart Based on the Birnbaum-Saunders Distribution Using Repetitive Sampling

In this paper, an attribute control chart using repetitive sampling is proposed when the lifetime of a product follows the Birnbaum Saunders distribution. The number of failures is to be monitored by designing two pairs of upper and lower control limits. The necessary measurements are derived to assess the average run length (ARL). The various tables for ARLs are presented when the scale parameter and/or the shape parameter are shifted. The efficiency of the proposed control chart is compared with an existing chart. The proposed chart is shown to be more efficient than an existing control chart in terms of ARL. A real example is given for illustration purpose.112Ysciescopu

A Multivariate Control Chart for Monitoring Several Exponential Quality Characteristics Using EWMA

In this paper, we propose a new multivariate control chart for monitoring several exponential distributed characteristics. We transform each exponential variable into the one following an approximately normal distribution. The control statistic of the proposed chart is constructed using the exponentially weighted moving average. The performance of the proposed control chart is studied using the average run length according to the process shift. Some tables are presented for bivariate and trivariate cases. The application of the proposed control chart is discussed with the help of simulated data.11Ysciescopu

An efficient multivariate feature ranking method for gene selection in high-dimensional microarray data

Classification of microarray data plays a significant role in the diagnosis and prediction of cancer. However, its high-dimensionality (>tens of thousands) compared to the number of observations (<tens of hundreds) may lead to poor classification accuracy. In addition, only a fraction of genes is really important for the classification of a certain cancer, and thus feature selection is very essential in this field. Due to the time and memory burden for processing the high-dimensional data, univariate feature ranking methods are widely-used in gene selection. However, most of them are not that accurate because they only consider the relevance of features to the target without considering the redundancy among features. In this study, we propose a novel multivariate feature ranking method to improve the quality of gene selection and ultimately to improve the accuracy of microarray data classification. The method can be efficiently applied to high-dimensional microarray data. We embedded the formal definition of relevance into a Markov blanket (MB) to create a new feature ranking method. Using a few microarray datasets, we demonstrated the practicability of MB-based feature ranking having high accuracy and good efficiency. The method outperformed commonly-used univariate ranking methods and also yielded the better result even compared with the other multivariate feature ranking method due to the advantage of data efficiency

Mixed control charts using EWMA Statistics

In this paper, two mixed control charts are designed for process monitoring when the quality characteristic of interest follows a normal distribution. The mixed control chart starts with monitoring the number of non-conforming items but switches to monitoring using exponentially weighted moving average (EWMA) statistic or hybrid EWMA statistic when the decision is indeterminate with the attribute data. The average run lengths are calculated to evaluate the performance of the proposed control charts according to the mean shift. The performance of both control charts is compared with each other and with the existing control chart. Simulation study is given to demonstrate the efficiency of the proposed control charts.1150Ysciescopu

A New S-2 Control Chart Using Multiple Dependent State Repetitive Sampling

The combined application of multiple dependent state sampling and the repetitive group sampling (RGS) is an efficient sampling scheme for industrial process monitoring as it combines the advantages of both the sampling schemes. In this paper, a new variance control chart has been proposed, when the interesting quality characteristic follows the normal distribution using the combination of these two efficient sampling schemes called multiple dependent state repetitive sampling. The control chart coefficients and parameters have been estimated through simulation for the in-control process by considering the target in-control average run lengths under different process settings. The efficiency of the proposed chart has been determined by computing the out-of-control ARL for different shift levels. The advantages of the proposed monitoring scheme have been discussed and compared with the existing RGS scheme and the single sampling scheme. A simulated example and a real industrial data have been included to demonstrate the application of the proposed monitoring scheme. It has been observed that the proposed chart is a valuable addition to the toolkit of the quality monitoring personnel.11Ysciescopu