Showing papers by "Huiping Cao published in 2021"
TL;DR: It is suggested that JRC, MC, and XC steers can be developed to slaughter weights in 30-mo using a rangeland-based grass-fed protocol, and that Jango Criollo steers exhibit desirable grazing behaviors previously observed in JRC cows.
14 citations
TL;DR: A novel problem of identifying significant PVs from MTS datasets is formulated and a solution framework, CNN-LR, is proposed that implements a new feature identification approach as X in the CNN network and can embed other feature extraction techniques (as X
Abstract: Multivariate time series (MTS) are collected for different variables in studying scientific phenomena or monitoring system health where each time series records the values of one variable for a time period. Among the different variables, it is common that only a few variables contribute significantly to a specific phenomenon. Furthermore, the variables contributing significantly to different phenomena are often different. We denote the different variables that contribute to the occurrences of different phenomena as Phenomenon-specific Variables (PVs) . In this paper, we formulate a novel problem of identifying significant PVs from MTS datasets. To analyze MTS data, feature extraction techniques have been extensively studied. However, most of them identify important global features for one dataset and do not utilize the temporal order of time series. To solve the newly introduced problem, we propose a solution framework, CNN $_{mts}$ m t s -X , which is a new variant of the Convolutional Neural Networks ( CNN ) and can embed other feature extraction techniques (as X ). Furthermore, we design a CNN $_{mts}$ m t s -LR method that implements a new feature identification approach ( LR ) as X in the CNN $_{mts}$ m t s -X framework. The LR method leverages both Linear Discriminant Analysis ( LDA ) and Random Forest ( RF ). Our extensive experiments on five real datasets show that the CNN $_{mts}$ m t s -LR method has exhibited much better performance than several other baseline methods. Using 30 percent of the PVs discovered from the CNN $_{mts}$ m t s -LR , classifications can achieve better or similar performance than using all the variables.
8 citations
26 Oct 2021
TL;DR: In this article, a segment-based Bayesian change point detection algorithm was proposed to detect abrupt changes over short periods of time, where a window of data points within a time series, rather than a single data point, was examined to determine if the window captures abrupt change.
Abstract: Change point detection is widely used for finding transitions between states of data generation within a time series. Methods for change point detection currently assume this transition is instantaneous and therefore focus on finding a single point of data to classify as a change point. However, this assumption is flawed because many time series actually display short periods of transitions between different states of data generation. Previous work has shown Bayesian Online Change Point Detection (BOCPD) to be the most effective method for change point detection on a wide range of different time series. This paper explores adapting the change point detection algorithms to detect abrupt changes over short periods of time. We design a segment-based mechanism to examine a window of data points within a time series, rather than a single data point, to determine if the window captures abrupt change. We test our segment-based Bayesian change detection algorithm on 36 different time series and compare it to the original BOCPD algorithm. Our results show that, for some of these 36 time series, the segment-based approach for detecting abrupt changes can much more accurately identify change points based on standard metrics.
2 citations
11 May 2021
TL;DR: In this article, the authors present a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews, which can reduce prediction errors while learning features better from the data.
Abstract: An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data.