In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the fin...

This paper present a new automated detection method for cardiac arrhythmia. The detection system is implemented with integration of feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using autoregressive (AR) and multivariate autoregressive (MVAR) modeling of one-lead and two-lead electrocardiogram si...

A multivariate polynomial is stable if it is nonvanishing whenever all variables have positive imaginary parts. We classify all linear partial differential operators in the Weyl algebra An that preserve stability. An important tool that we develop in the process is the higher dimensional generalization of Pólya-Schur’s notion of multiplier sequence. We characterize all multivariate multiplier s...

Classification of texture images has been recognized as an important task in the field of image analysis and computer vision through the last few decades. A plethora of research papers have appeared in the literature trying to cope with effective ways to extract faithful distributions that accurately represent the inner content and attributes of texture images. An issue of great importance is, ...

Social media outlets, such as Twitter, provide invaluable information for understanding the social and political climate surrounding particular issues. Millions of people who vary in age, social class, and political beliefs come together in conversation. However, this information poses challenges to making inferences from these tweets. Using the tweets from the 2016 U.S. Presidential campaign, ...

Multivariate time series (MTS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new dista...

In this paper we consider classification tasks where the class depends on the co-evolution of multiple variables over time, for instance, “if A happens before B and in the meantime we do not observe C, then we have a failure of class X”. We present a two-phased approach to derive such patterns from data. In the first step, we seek the most specific pattern that still matches all data from one c...

Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. This paper presents a method for classifying EEG during motor-imagery by the combination of well-known common spatial pattern (CSP) with so-called multivariate empirical mode decomposition (MEMD), which ...

Understanding that various tree species have characteristics similar to each other, it follows that some type of hierarchical classification scheme could be used to identify species using LIDAR data. Cluster analysis, one of the unsupervised classification methods, was conducted for all individual trees using the k-medoid algorithm. Instead of using one-step cluster analysis, a stepwise cluster...

Multivariate statistical analysis is an important data analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither par...