Shih-Wei Lin

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Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without(More)
In this study, we consider the application of a simulated annealing (SA) heuristic to the truck and trailer routing problem with time windows (TTRPTW), an extension of the truck and trailer routing problem (TTRP). TTRP is a variant of the well-known well-studied vehicle routing problem (VRP). In TTRP, some customers can be serviced by either a complete(More)
To promote the classification accuracy and decrease the time of extracting features and finding (near) optimal classification model of an ultrasound breast tumor image computer-aided diagnosis system, we propose an approach which simultaneously combines feature selection and parameter setting in this study. In our approach ultrasound breast tumors were(More)
INTRODUCTION Diffuse alveolar damage (DAD) is the pathological hallmark of acute respiratory distress syndrome (ARDS), however, the presence of DAD in the clinical criteria of ARDS patients by Berlin definition is little known. This study is designed to investigate the role of DAD in ARDS patients who underwent open lung biopsy. METHODS We retrospectively(More)
Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that(More)
Linear discriminant analysis (LDA) is a commonly used classification method. It can provide important weight information for constructing a classificationmodel. However, real-world data sets generally have many features, not all of which benefit the classification results. If a feature selection algorithm is not employed, unsatisfactory classification will(More)
Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is(More)