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An Efficient Smooth Quantile Boost Algorithm for binary classification

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Advances in Pure Mathematics 2016年第9期6卷 615-624页

作者： Zhefeng Wang Wanzhou Ye Department of Mathematics College of Science Shanghai University Shanghai China

In this paper, we propose a Smooth Quantile Boost classification (SQBC) algorithm for binary classification problem. The SQBC algorithm directly uses a smooth function to approximate the “check function” of the quantile regression. Compared to other boost-based classification algorithms, the proposed algorithm is more accurate, flexible and robust to noisy predictors. Furthermore, the SQBC algorithm also can work well in high dimensional space. Extensive numerical experiments show that our proposed method has better performance on randomly simulations and real data.

关键词： Boosting Quantile Regression Smooth Check Function binary classification

Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class classification Tool for the GI Tract Anomalies by Using binary CNNs

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Journal of Biomedical Science and Engineering 2021年第12期14卷 402-414页

作者： Christos Barbagiannis Alexios Polydorou Michail Zervakis Andreas Polydorou Eleftheria Sergaki School of Electrical and Computer Engineering (ECE) School Technical University of Crete Chania Greece Department of Surgery School of Medicine National and Kapodistrian University of Athens Athens Greece

The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam(R) Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm v

关键词： Capsule Endoscopy (CE) Small Bowel Bleeding (SBB) Angioectasia Haemorrhage Gatrointestinal (GI) Small Bowel Capsule Endoscopy (SBCE) Convolutional Neural Network (CNN) Computer Aided Diagnosis (CAD) Image Level Annotation Pixel Level Annotation binary classification

Cost-sensitive multi-layer perceptron for binary classification with imbalanced data

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Cost-sensitive multi-layer perceptron for binary classificat...

Tumor classification of Gene Expression Data by Fuzzy Hybrid Twin SVM

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作者： Zheng Liu Sen Zhang Wendong Xiao Yan Di School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing

Currently, class imbalance has been a challenge for classification due to its highly imbalanced instances of distinct classes. With the advantage in quantity, the majority classes can get high accuracy in classification while many instances belonging to minority classes are inclined to be classified as majority classes. In this paper, we propose a novel cost-sensitive method based on multi-layer perceptron（CMMLP） for binary classification with imbalanced data. The proposed cost matrix is used to modify the construction of loss function so as to encourage classifier to pay more attention to the accuracy of minority class by minimizing training error. In order to verify the effectiveness of CMMLP, CMMLP is applied to some benchmark datasets and Click-Through Rate（CTR） prediction datasets. Experimental results illustrate that the new cost-sensitive approach can achieve better performance for binary classification with imbalanced data than the original MLP（OMLP）.

关键词： Imbalance learning binary classification CMMLP CTR prediction

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Chinese Journal of Electronics 2022年第1期31卷 99-106页

作者： DUAN Hua FENG Tong LIU Songning ZHANG Yulin SU Jionglong College of Mathematics and Systems Science Shandong University of Science and Technology School of AI and Advanced Computing XJTLU Entrepreneur College (Taicang) Xi'an Jiaotong-Liverpool University

A new classification model, the fuzzy hybrid twin support vector machine(TWSVM), namely FHTWSVM, is proposed by combining the fuzzy TWSVM and the hypersphere support vector machine(SVM).The hypersphere SVM is utilized for generating the hyperspheres for the positive and negative class with the smallest possible radius, so that the hyperspheres can contain as many samples as possible. The samples which the hyperspheres cover form a new sample set. Furthermore a distance-based fuzzy function is utilized to calculate the fuzzy factors for the samples. Finally FHTWSVM is used to train all samples with the parameters optimized by grid search. This method can maximize intra-class clustering for noise removal and reduce the influence of outliers.To demonstrate the superiority of the performance of FHTWSVM over other classifiers, e.g., KNN, RF,Bayesian, TWSVM, Ada Boost and XGBoost, a series of experiments is conducted using eight gene expression datasets. The evaluation results show that the proposed approach can improve the classification performance as well as reduce prediction errors for the datasets.

关键词： binary classification Hybrid twin SVM Fuzzy factor Grid search

Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

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Computers, Materials & Continua 2023年第2期74卷 3063-3083页

作者： Sukhendra Singh Sur Singh Rawat Manoj Gupta B.K.Tripathi Faisal Alanzi Arnab Majumdar Pattaraporn Khuwuthyakorn Orawit Thinnukool Department of Information Technology JSS Academy of Technical EducationNoidaIndia JSS Academy of Technical Education NoidaIndia Department of Electronics and Communication Engineering JECRC University JaipurRajasthanIndia Harcourt Butler Technological University Kanpur India Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Currently,breast cancer has been amajor cause of deaths in women worldwide and the World Health Organization(WHO)has confirmed this.The severity of this disease can be minimized to the large extend,if it is diagnosed properly at an early stage of the disease.Therefore,the proper treatment of a patient having cancer can be processed in better way,if it can be diagnosed properly as early as possible using the better algorithms.Moreover,it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues.To address the above said issues,this paper presents a hybrid model using the transfer learning to study the histopathological images,which help in detection and rectification of the disease at a low cost.Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper.The experimental results show that the proposed model outperformed the baseline methods,with F-scores of 0.81 for DenseNet+Logistic Regression hybrid model,(F-score:0.73)for Visual Geometry Group(VGG)+Logistic Regression hybrid model,(F-score:0.74)for VGG+Random Forest,(F-score:0.79)for DenseNet+Random Forest,and(F-score:0.79)for VGG+Densenet+Logistic Regression hybrid model on the dataset of histopathological images.

关键词： Histopathological deep neural network machine learning breast cancer binary classification transfer learning

Enhanced history matching process by incorporation of saturation logs as model selection criteria

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Petroleum Exploration and Development 2023年第2期50卷 450-463页

作者： APONTE Jesus Manuel WEBBER Robert CENTENO Maria Astrid DHAKAL Hom Nath SAYED Mohamed Hassan MALAKOOTI Reza Faculty of Technology University of PortsmouthPortsmouth PO12UPUnited Kingdom CNOOC International Ltd Uxbridge UB81LUUnited Kingdom London South Bank University London SE10AAUnited Kingdom University of Southampton Southampton SO171BJUnited Kingdom Computer Modelling Group Ltd London OX108BAUnited Kingdom

This paper proposes a methodology for an alternative history matching process enhanced by the incorporation of a simplified binary interpretation of reservoir saturation logs(RST) as objective function. Incorporating fluids saturation logs during the history matching phase unlocks the possibility to adjust or select models that better represent the near wellbore waterfront movement, which is particularly important for uncertainty mitigation during future well interference assessments in water driven reservoirs. For the purposes of this study, a semi-synthetic open-source reservoir model was used as base case to evaluate the proposed methodology. The reservoir model represents a water driven, highly heterogenous sandstone reservoir from Namorado field in Brazil. To effectively compare the proposed methodology against the conventional methods, a commercial reservoir simulator was used in combination with a state-of-the-art benchmarking workflow based on the Big LoopTMapproach. A well-known group of binary metrics were evaluated to be used as the objective function, and the Matthew correlation coefficient(MCC) has been proved to offer the best results when using binary data from water saturation logs. History matching results obtained with the proposed methodology allowed the selection of a more reliable group of reservoir models,especially for cases with high heterogeneity. The methodology also offers additional information and understanding of sweep behaviour behind the well casing at specific production zones, thus revealing full model potential to define new wells and reservoir development opportunities.

关键词： geological modeling reservoir model objective function binary classification history matching saturation logs

The balance property in neural network modelling

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Statistical Theory and Related Fields 2022年第1期6卷 1-9页

作者： Mario V.Wüthrich RiskLab Department of MathematicsETH ZurichZurichSwitzerland

In estimation and prediction theory,considerable attention is paid to the question of hav-ing unbiased estimators on a global population level.Recent developments in neural network modelling have mainly focused on accuracy on a granular sample level,and the question of unbi-asedness on the population level has almost completely been neglected by that community.We discuss this question within neural network regression models,and we provide methods of receiving unbiased estimators for these models on the global population level.

关键词： Balance property unbiased binary classification logistic regression neural network classification tree

Valuable Data Extraction for Resistivity Imaging Logging Interpretation

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Tsinghua Science and Technology 2020年第2期25卷 281-293页

作者： Yili Ren Renbin Gong Zhou Feng Meichao Li Institute of Computer Application Technology PetroChina Research Institute of Petroleum Exploration and Development(RIPED)Beijing 100083China Department of Well Logging&Remote Sensing Technology of RIPED Beijing 100083China

Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

关键词： machine learning binary classification multiclass classification outlier detection imaging logging

A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

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Journal of Innovative Optical Health Sciences 2021年第6期4卷 81-89页

作者： Sang Hee Jo Yoonhee Kim Yoon Bum Lee Sung Suk Oh Jong-ryul Choi School of Biomedical Engineering Daegu Catholic University(DCU)Gyeongsan 38430Republic of Korea Medical Device Development Center Daegu-Gyeongbuk Medical Innovation Foundation(DGMIF) Daegu 41061Republic of Korea Laboratory Animal Center Daegu-Gyeongbuk Medical Innovation Foundation(DGMIF) Daegu 41061Republic of Korea

Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms.In this study,we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains.Six machine learning-based algorithms for binary classification were applied,and the accu-racies were compared to classify normal tissues and photothrombotic lesions.The lesion classification model consisting of a 3-layered neural network with a rectified linear unit(ReLU)activation function,Xavier initialization,and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy(0.975).In the validation using the tested histological images,it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke.Through the development of machine learning-based photothrombotic lesion classi-fication models and performance comparisons,we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

关键词： Machine learning histopathological images photothrombotic lesion rabbit brain binary classification logistic regression multi-layer neural networks