Data Mining - Concepts and Techniques.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

Trends in extreme learning machines

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Machine Learning in Agriculture: A Review.

The epileptic state classification for preictal prediction of seizure is essential for preventing the damages caused by seizure. Existing research mainly focus on the epileptic seizure detection while few of them investigate the preictal state prediction. In this paper. a novel electroencephalograph signal (EEG) based prediction algorithm for the preictal state of seizure is developed. The one hour preictal state before the seizure is divided into three non-overlapped segments and a multi-class classification is then performed for preictal state prediction. The wavelet packet based features (WPFs), including the subband energy ratio and three wavelet entropies, are extracted for the multi-channel EEG signal representation. The Random Forest algorithm (RF) is adopted for the preictal state prediction. Experiments on the benchmark CHB-MIT EEG database are conducted for performance analysis. Comparisons with methods based on popular EEG features and classifiers are provided to show the superiority of the proposed WPFs+RF based preictal state prediction algorithm.

Epileptic State Classification for Seizure Prediction with Wavelet Packet Features and Random Forest

This paper provides improved time delay-dependent stability criteria for multi-input and multi-output (MIMO) network control systems (NCSs) with nonlinear perturbations. Without the stability assumption on the neutral operator after the descriptor approach, the new proposed stability theory is less conservative than the existing stability condition. Theoretical proof is given in this paper to demonstrate the effectiveness of the proposed stability condition.

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Improved delay-dependent stability conditions for MIMO networked control systems with nonlinear perturbations.

Deformation of gait silhouettes caused by different view angles heavily affects the performance of gait recognition. In this paper, a new method based on deterministic learning and knowledge fusion is proposed to eliminate the effect of view angle for efficient view-invariant gait recognition. First, the binarized walking silhouettes are characterized with three kinds of time-varying width parameters. The nonlinear dynamics underlying different individuals’ width parameters is effectively approximated by radial basis function (RBF) neural networks through deterministic learning algorithm. The extracted gait dynamics captures the spatio-temporal characteristics of human walking, represents the dynamics of gait motion, and is shown to be insensitive to the variance across various view angles. The learned knowledge of gait dynamics is stored in constant RBF networks and used as the gait pattern. Second, in order to handle the problem of view change no matter the variation is small or large, the learned knowledge of gait dynamics from different views is fused by constructing a deep convolutional and recurrent neural network (CRNN) model for later human identification task. This knowledge fusion strategy can take advantage of the encoded local characteristics extracted from the CNN and the long-term dependencies captured by the RNN. Experimental results show that promising recognition accuracy can be achieved.

Human gait recognition based on deterministic learning and knowledge fusion through multiple walking views

Imbalanced learning algorithm based intelligent abnormal electricity consumption detection

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

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Jiuwen Cao

Papers

Epileptic State Classification for Seizure Prediction with Wavelet Packet Features and Random Forest

Improved delay-dependent stability conditions for MIMO networked control systems with nonlinear perturbations.

Human gait recognition based on deterministic learning and knowledge fusion through multiple walking views

Imbalanced learning algorithm based intelligent abnormal electricity consumption detection

Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features