Predicting the Probability of Exceeding Critical System Thresholds

In this paper we show how regression modelling can be combined with a special kind of data transformation technique that improves model precision and produces several “preliminary” estimates of the target value. These preliminary estimates can be used for interval estimates of the target value as well as for predicting the probability that it has or will exceed arbitrary predefined thresholds. Our approach can be combined with various regression models and applied in many domains that need to estimate the probability of system malfunctions or other hazardous states brought about by system variables exceeding critical safety thresholds. We rigorously derive the formulas for the probability of crossing an upper bound and a lower bound both separately (one-sided intervals) and together (a two-sided interval), and verify the approach experimentally on a real dataset from the electric power industry.У цій статті показано, як регресійне моделювання можна комбінувати зі спеціальним видом перетворення даних, яке покращує точність моделі і дає кілька «попередніх» оцінок цільового значення. Ці попередні оцінки можна використовувати для інтервальних оцінок цільового значення, а також для прогнозування ймовірності того, що воно прийме або перевищить довільні попередньо визначені порогові значення. Наш підхід можна комбінувати з різними регресійними моделями і застосовувати в багатьох областях, які повинні оцінювати вірогідність збоїв системи або інших небезпечних станів, викликаних системними змінними, що перевищують критичні пороги безпеки. Ми строго виводимо формули для ймовірності перетину верхньої та нижньої межі як окремо (односторонні інтервали), так і разом (двосторонній інтервал) і перевіряємо наш підхід експериментально на реальному наборі даних з електроенергетики.В этой статье показано, как регрессионное моделирование можно комбинировать со специальным видом преобразования данных, которое улучшает точность модели и дает несколько «предварительных» оценок целевого значения. Эти предварительные оценки могут использоваться для интервальных оценок целевого значения, а также для прогнозирования вероятности того, что оно примет или превысит произвольные предопределенные пороговые значения. Наш подход можно комбинировать с различными регрессионными моделями и применять во многих областях, которые должны оценивать вероятность сбоев системы или других опасных состояний, вызванных системными переменными, превышающими критические пороги безопасности. Мы строго выводим формулы для вероятности пересечения верхней и нижней границы как отдельно (односторонние интервалы), так и вместе (двухсторонний интервал), и проверяем наш подход экспериментально на реальном наборе данных из электроэнергетики

Ensemble Learning for Free with Evolutionary Algorithms ?

Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.<br /

Multiple Classifier System for Remote Sensing Image Classification: A Review

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community

Fully automated platelet differential interference contrast image analysis via deep learning

Platelets mediate arterial thrombosis, a leading cause of myocardial infarction and stroke. During injury, platelets adhere and spread over exposed subendothelial matrix substrates of the damaged blood vessel wall. The mechanisms which govern platelet activation and their interaction with a range of substrates are therefore regularly investigated using platelet spreading assays. These assays often use differential interference contrast (DIC) microscopy to assess platelet morphology and analysis performed using manual annotation. Here, a convolutional neural network (CNN) allowed fully automated analysis of platelet spreading assays captured by DIC microscopy. The CNN was trained using 120 generalised training images. Increasing the number of training images increases the mean average precision of the CNN. The CNN performance was compared to six manual annotators. Significant variation was observed between annotators, highlighting bias when manual analysis is performed. The CNN effectively analysed platelet morphology when platelets spread over a range of substrates (CRP-XL, vWF and fibrinogen), in the presence and absence of inhibitors (dasatinib, ibrutinib and PRT-060318) and agonist (thrombin), with results consistent in quantifying spread platelet area which is comparable to published literature. The application of a CNN enables, for the first time, automated analysis of platelet spreading assays captured by DIC microscopy