scholarly journals A HIERARCHICAL CLASSIFIER FOR MULTICLASS PROSTATE HISTOPATHOLOGY IMAGE GLEASON GRADING

2018 ◽  
Author(s):  
Dheeb Albashish ◽  
Shahnorbanun Sahran ◽  
Azizi Abdullah ◽  
Afzan Adam ◽  
Mohammed Alweshah
Keyword(s):  
Domain Knowledge ◽  
High Efficiency ◽  
Binary Classification ◽  
Class Imbalance ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Classification Performance ◽  
Divide And Conquer ◽  
Multiclass Problem ◽  
Histopathology Images

Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and one- versus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed. However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the ‘divide-and-conquer’ principle by allocating each binary task into two separate subtasks; strong and weak, based on the power of the samples in each binary task. Conversely, the strong samples include more information about the considered task, which motivates the production of the final prediction. Experimental results on prostate histopathological images illustrated that the MLA significantly outperforms the Ovall and OVO approaches when applied to the ensemble framework. The results also confirmed the high efficiency of the ensemble framework with the MLA scheme in dealing with the multiclass classification problem.

A Scalable Classification Algorithm for Very Large Datasets

2005 ◽  
Vol 04 (02) ◽  
pp. 83-94
Author(s):  
Dursun Delen ◽  
Marilyn G. Kletke ◽  
Jin-Hwa Kim
Keyword(s):  
Domain Knowledge ◽  
Binary Classification ◽  
Performance Testing ◽  
Classification Problem ◽  
Large Datasets ◽  
Classification Algorithm ◽  
Iterative Refinement ◽  
Classification Algorithms ◽  
Very Large Datasets ◽  
Scalable Classification

Today's organisations are collecting and storing massive amounts of data from their customer transactions and e-commerce/e-business applications. Many classification algorithms are not scalable to work effectively and efficiently with these very large datasets. This study constructs a new scalable classification algorithm (referred to in this manuscript as Iterative Refinement Algorithm, or IRA in short) that builds domain knowledge from very large datasets using an iterative inductive learning mechanism. Unlike existing algorithms that build the complete domain knowledge from a dataset all at once, IRA builds the initial domain knowledge from a subset of the available data and then iteratively improves, sharpens and polishes it using the chucks from the remaining data. Performance testing of IRA on two datasets (one with approximately five million records for a binary classification problem and another with approximately 600 K records for a seven-class classification problem) resulted in more accurate domain knowledge as compared to other prediction methods including logistic regression, discriminant analysis, neural networks, C5, CART and CHAID. Unlike other classification algorithms whose performance and accuracy deteriorate as data size increases, the efficacy of IRA improves as datasets become significantly larger.

scholarly journals Multiclass Classification in the Problem of Differential Diagnosis of Venous Diseases Based on Microwave Radiometry Data

2021 ◽  
Vol 12 (2) ◽  
pp. 37-52
Author(s):  
Vladislav Viktorovich Levshinskii
Keyword(s):  
Differential Diagnosis ◽  
Binary Classification ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Microwave Radiometry ◽  
Classification Model ◽  
Multivariate Logistic Regression Model ◽  
Accuracy Score ◽  
Classification Problems ◽  
Venous Diseases

This article is devoted to applying mathematical models in the differential diagnosis of venous diseases based on microwave radiometry data. A modified approach for transforming feature space in thermometric data is described. After constructing features, a multiclass classification problem is solved in several ways: by reducing to binary classification problems using “one versus rest” and “one versus one” methods and building a multivariate logistic regression model. The best classification model achieved an average balanced accuracy score of 0.574. A key feature of the approach is that classification result can be explained and justified in terms understandable to a diagnostician. This article presents the most significant patterns in thermometric data and the accuracy with which they can identify different classes of diseases.

Huber collaborative representation for robust multiclass classification

2019 ◽  
Vol 17 (04) ◽  
pp. 1950020 ◽  
Author(s):  
Cuiming Zou ◽  
Yuan Yan Tang ◽  
Yulong Wang ◽  
Zhenghua Luo
Keyword(s):  
Robust Statistics ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Classification Performance ◽  
Collaborative Representation ◽  
Proposed Model ◽  
The Mean ◽  
Classification Tasks ◽  
Real World Datasets ◽  
The Cost

Recent advances have shown a great potential of collaborative representation (CR) for multiclass classification. However, conventional CR-based classification methods adopt the mean square error (MSE) criterion as the cost function, which is sensitive to gross corruption and outliers. To address this limitation, inspired by the success of robust statistics, we develop a Huber collaborative representation-based classification (HCRC) method for robust multiclass classification. Concretely, we cast the classification problem as a Huber collaborative representation problem with the Huber estimator. Our another contribution is to design an efficient half-quadratic (HQ) algorithm with guaranteed convergence to solve the proposed model efficiently. Furthermore, we also give a theoretical analysis of the classification performance of HCRC. Experiments on real-world datasets corroborate that HCRC is an effective and robust algorithm for multiclass classification tasks.

scholarly journals FEB-Stacking and FEB-DNN Models for Stock Trend Prediction: A Performance Analysis for Pre and Post Covid-19 Periods

2021 ◽  
Vol 4 (1) ◽  
pp. 51-86 ◽  
Author(s):  
Indranil Ghosh ◽  
◽  
Tamal Datta Chaudhuri ◽  
Keyword(s):  
Performance Analysis ◽  
Stock Price ◽  
Binary Classification ◽  
Class Imbalance ◽  
Principal Component ◽  
Classification Problem ◽  
Feature Engineering ◽  
Trend Prediction ◽  
Neural Network Models ◽  
Stock Price Prediction

In this paper, stock price prediction is perceived as a binary classification problem where the goal is to predict whether an increase or decrease in closing prices is going to be observed the next day. The framework will be of use for both investors and traders. In the aftermath of the Covid-19 pandemic, global financial markets have seen growing uncertainty and volatility and as a consequence, precise prediction of stock price trend has emerged to be extremely challenging. In this background, we propose two integrated frameworks wherein rigorous feature engineering, methodology to sort out class imbalance, and predictive modeling are clubbed together to perform stock trend prediction during normal and new normal times. A number of technical and macroeconomic indicators are chosen as explanatory variables, which are further refined through dedicated feature engineering process by applying Kernel Principal Component (KPCA) analysis. Bootstrapping procedure has been used to deal with class imbalance. Finally, two separate Artificial Intelligence models namely, Stacking and Deep Neural Network models are deployed separately on feature engineered and bootstrapped samples for estimating trends in prices of underlying stocks during pre and post Covid-19 periods. Rigorous performance analysis and comparative evaluation with other well-known models justify the effectiveness and superiority of proposed frameworks.

scholarly journals Comparing Multiclass, Binary, and Hierarchical Machine Learning Classification schemes for variable stars

2019 ◽  
Vol 488 (4) ◽  
pp. 4858-4872 ◽  
Author(s):  
Zafiirah Hosenie ◽  
Robert J Lyon ◽  
Benjamin W Stappers ◽  
Arrykrishna Mootoovaloo
Keyword(s):  
Machine Learning ◽  
Binary Stars ◽  
Binary Classification ◽  
Classification Performance ◽  
Variable Stars ◽  
Machine Learning Algorithms ◽  
Accurate Identification ◽  
Data Set ◽  
Machine Learning Classification ◽  
Multiclass Problem

ABSTRACT Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data describing 11 types of variable stars from the Catalina Real-Time Transient Survey (CRTS), we illustrate how to capture the most important information from computed features and describe detailed methods of how to robustly use information theory for feature selection and evaluation. We apply three machine learning algorithms and demonstrate how to optimize these classifiers via cross-validation techniques. For the CRTS data set, we find that the random forest classifier performs best in terms of balanced accuracy and geometric means. We demonstrate substantially improved classification results by converting the multiclass problem into a binary classification task, achieving a balanced-accuracy rate of ∼99 per cent for the classification of δ Scuti and anomalous Cepheids. Additionally, we describe how classification performance can be improved via converting a ‘flat multiclass’ problem into a hierarchical taxonomy. We develop a new hierarchical structure and propose a new set of classification features, enabling the accurate identification of subtypes of Cepheids, RR Lyrae, and eclipsing binary stars in CRTS data.

scholarly journals A Learning Framework of Nonparallel Hyperplanes Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Zhi-Xia Yang ◽  
Yuan-Hai Shao ◽  
Yao-Lin Jiang
Keyword(s):  
Binary Classification ◽  
Computational Cost ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Decision Function ◽  
Support Vector ◽  
Learning Framework ◽  
Hinge Loss ◽  
Benchmark Datasets ◽  
Nonparallel Hyperplanes

A novel learning framework of nonparallel hyperplanes support vector machines (NPSVMs) is proposed for binary classification and multiclass classification. This framework not only includes twin SVM (TWSVM) and its many deformation versions but also extends them into multiclass classification problem when different parameters or loss functions are chosen. Concretely, we discuss the linear and nonlinear cases of the framework, in which we select the hinge loss function as example. Moreover, we also give the primal problems of several extension versions of TWSVM’s deformation versions. It is worth mentioning that, in the decision function, the Euclidean distance is replaced by the absolute value|wTx+b|, which keeps the consistency between the decision function and the optimization problem and reduces the computational cost particularly when the kernel function is introduced. The numerical experiments on several artificial and benchmark datasets indicate that our framework is not only fast but also shows good generalization.

scholarly journals Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Binbin Wang ◽  
Li Xiao ◽  
Yang Liu ◽  
Jing Wang ◽  
Beihong Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Binary Classification ◽  
Characteristic Curve ◽  
Classification Problem ◽  
Retinal Hemorrhage ◽  
Multiclass Classification ◽  
Deep Convolutional Neural Network ◽  
Cross Sectional

There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In present study, we develop a deep convolutional neural network (DCNN) for automated classification and grading of retinal hemorrhage. We used 48,996 digital fundus images from 3770 newborns with retinal hemorrhage of different severity (grade 1, 2 and 3) and normal controls from a large cross-sectional investigation in China. The DCNN was trained for automated grading of retinal hemorrhage (multiclass classification problem: hemorrhage-free and grades 1, 2 and 3) and then validated for its performance level. The DCNN yielded an accuracy of 97.85 to 99.96%, and the area under the receiver operating characteristic curve was 0.989–1.000 in the binary classification of neonatal retinal hemorrhage (i.e., one classification vs. the others). The overall accuracy with regard to the multiclass classification problem was 97.44%. This is the first study to show that a DCNN can detect and grade neonatal retinal hemorrhage at high performance levels. Artificial intelligence will play more positive roles in ocular healthcare of newborns and children.

scholarly journals Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1550
Author(s):  
Alexandros Liapis ◽  
Evanthia Faliagka ◽  
Christos P. Antonopoulos ◽  
Georgios Keramidas ◽  
Nikolaos Voros
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Experience ◽  
Electrodermal Activity ◽  
Binary Classification ◽  
Research Question ◽  
Classification Problem ◽  
Categorical Variables ◽  
Stress Detection ◽  
Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

scholarly journals Signal Classification Algorithms over Time Selective Channels

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1714
Author(s):  
Mohamed Marey ◽  
Hala Mostafa
Keyword(s):  
Block Code ◽  
Classification Problem ◽  
Classification Performance ◽  
Signal Classification ◽  
Final Decision ◽  
Time Block ◽  
Channel Response ◽  
Over Time ◽  
Parallel Fashion

In this work, we propose a general framework to design a signal classification algorithm over time selective channels for wireless communications applications. We derive an upper bound on the maximum number of observation samples over which the channel response is an essential invariant. The proposed framework relies on dividing the received signal into blocks, and each of them has a length less than the mentioned bound. Then, these blocks are fed into a number of classifiers in a parallel fashion. A final decision is made through a well-designed combiner and detector. As a case study, we employ the proposed framework on a space-time block-code classification problem by developing two combiners and detectors. Monte Carlo simulations show that the proposed framework is capable of achieving excellent classification performance over time selective channels compared to the conventional algorithms.

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