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.

LAC-GAN: Lesion attention conditional GAN for Ultra-widefield image synthesis

Objective: This study concentrates on subtle electrocardiogram (ECG) spatiotemporal characteristics in the repolarization phase, and describes a deterministic learning-based methodology for the detection of abnormal cardiac dynamics induced by ischemia. Methods: ST-T complex of the surface 12-lead ECG signals are identified and extracted. Cardiac dynamics underlying ST-T complex signals is captured using deterministic learning algorithm. This kind of dynamics information represents the beat-to-beat temporal change of electrophysiological modifications in ventricular repolarization, which is shown to be sensitive to the variance during myocardial ischemia. Cardiodynamicsgram (CDG) is proposed as the three-dimensional graphic representation of cardiac dynamics information. Results: Encouraging evaluation results are achieved on electrocardiograms from public PTB database and hospital patients. Significant correlations are found between the CDG morphology and ischemia. Conclusion: Anormal dynamics of cardiac repolarization during ischemia can be detected using a deterministic learning-based methodology. The extracted cardiac dynamics information within routine ECG is expected to provide early detection for latent ischemia before obvious pathological changes are present in ECG. Significance: The proposed techniques can be considered as a complementary tool to the generally accepted ECG method for detection of abnormal dynamics in cardiac repolarization, which are important for identifying patients at risk of myocardial ischemia.

Deterministic Learning-Based Methodology for Detecting Abnormal Dynamics of Cardiac Repolarization During Ischemia

WEST syndrome is an unknown etiology infant epilepsy, which is characterized by the flexion spastic seizure, intellectual motion development lag, electrode abnormalities, arrhythmia. In this brief, we present a novel electrocardiogram (ECG) based WEST syndrome epilepsy seizure detection method. Based on deterministic learning (DT) theory, the dynamic model of ECG is firstly constructed. The cardiodynamicsgrams (CDGs) of ECGs in seizure and interictal periods are then derived. Nonlinear features on CDGs are extracted for WEST syndrome characterization. For performance evaluation, experiments on ECGs of 12 WEST syndrome patients from the Children’s Hospital of Zhejiang University School of Medicine (CHZU) is carried out. The proposed method can obtain an average of <inline-formula> <tex-math notation="LaTeX">$94.49{\%}$ </tex-math></inline-formula> F1-score, <inline-formula> <tex-math notation="LaTeX">$93.76{\%}$ </tex-math></inline-formula> precision and <inline-formula> <tex-math notation="LaTeX">$95.58{\%}$ </tex-math></inline-formula> accuracy, that outperforms the heart rate variability (HRV) based methods.

Deterministic Learning-Based WEST Syndrome Analysis and Seizure Detection on ECG

Functional connectivity (FC) is an efficient measurement to describe brain’s traits in information processing. But FCs at developmental stages from infacy to adolescence usually have severe individual variance, may affect the FC characterization analysis. Yet few past studies try to address this problem. In this paper, we select quiet sleep (QS) and non-rapid eye movement (NREM) period electroencephalogram (EEG) of 42 healthy subjects from 0 to 17 years old for study. Random network combined with stability measurement using Shannon entropy (SE) is used to construct the individual-level functional connectivity (ILFC), which can describe the individual brain’s interaction. Majority voting is applied to construct the group-level functional connectivity (GLFC), which can describe the core part of ILFCs within the same age. Based on FCs, graph theory and statistical analysis are further applied, where the following conclusions are observed: (1) The β band of EEG is most important in showing the age related variance of functional separation and integration. (2) 3 months 3 years old is the transition period from intra-subnetwork interacting mode to inter-subnetwork interacting mode. (3) The phenomenon of complementation and overlay among GLFCs in different bands can be found. (4) The GLFC’s network centrality becomes not obvious as age increases during 1 14 years old. 

Graph Theory based Multi-level Cortical Functional Connectivity Developmental Analysis

Speech impairment assessment is crucial to the treatment evaluation of speech therapy. The current evaluation methods mainly depend on speech-language pathologists (SLPs). Automatic speech impairment assessment (ASIA), especially the regression of severity scores, has not received enough attention. So we present a novel ASIA algorithm which based on the multi-task leaning with joint severity level classification and score regression. Owing to the auxiliary classification task, the precision of the severity score prediction can be improved effectively. In addition, the residual network (ResNet) and the long short-term memory (LSTM) are cascaded as the backbone. The performance of the model is demonstrated on the Mandarin AphasiaBank dataset and the experiments show that the algorithm achieves promising performance.

Jiuwen Cao

Papers

LAC-GAN: Lesion attention conditional GAN for Ultra-widefield image synthesis

Deterministic Learning-Based Methodology for Detecting Abnormal Dynamics of Cardiac Repolarization During Ischemia

Deterministic Learning-Based WEST Syndrome Analysis and Seizure Detection on ECG

Graph Theory based Multi-level Cortical Functional Connectivity Developmental Analysis

A Novel Multi-task Learning based Automatic Speech Impairment Assessment Algorithm