The Best Binary Classification Books of All Time

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models.

This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Manyof these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll LearnCompute entropy to detect problematic predictors. Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing. Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling. Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising. Use Monte-Carlo permutation methods to assessthe role of good luck in performance results. Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions. Who This Book is For. Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery.

Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry. Provides insights into the theory, algorithms, implementation, and application of deep-learning techniques for medical images such as transfer learning using pretrained CNNs, series networks, directed acyclic graph networks, lightweight CNN models, deep feature extraction, and conventional machine learning approaches for feature selection, feature dimensionality reduction, and classification using support vector machine, neuro-fuzzy classifiersCovers the various augmentation techniques that can be used with medical images and the CNN-based CAC system designs for binary classification of medical images focusing on chest radiographsInvestigates the development of an optimal CAC system design with deep feature extraction and classification of chest radiographs by comparing the performance of 12 different CAC system designs.

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Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices.

In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features. Reviews recent advances in CNN compression and accelerationElaborates recent advances on binary neural network (BNN) technologiesIntroduces applications of BNN in image classification, speech recognition, object detection, and more.

Over the years, a considerable body of research has accumulated on classification analysis, with its usefulness demonstrated in various fields, including engineering, medical and social sciences, economics, marketing, finance, education and management. In this book, eight classification rules for binary variables were considered. A simulation experiment was conducted to compare the performance of all these rules. The eight classification procedures are discussed and evaluated at 118 configurations of the sampling experiments.

From the analysis carried out, the results obtained ranked the procedures as follows: Optimal, Linear discriminant, Maximum likelihood, Predictive, Dillon-Goldstein, Full multinomial, Likelihood ratio and Nearest neighbour rule. Whereas for three and four variables, the maximum likelihood rule is preferred and for five variables optimal rule is preferred in terms of minimizing the expected actual error rate. The overall best method is the optimal classification rule. This book is intended to be useful to professionals in medical, Education, Social sciences, finance or anyone studying probability and multivariate data statistics at sub-degree and degree levels.

This book presents and discusses the numerous measures of test performance that can be derived from 2x2 tables. Worked examples based on pragmatic test accuracy study data are used in chapters to illustrate relevance to day-to-day clinical practice. Readers will gain a good understanding of sensitivity and specificity and predictive values along with many other parameters. The contents are highly structured and the use of worked examples facilitates understanding and interpretation.

This book is a resource for clinicians in any discipline who are involved in the performance or assessment of test accuracy studies, and professionals in the disciplines of machine learning or informatics wishing to gain insight into clinical applications of 2x2 tables.

DNA microarray technology has greatly influenced the realms of biomedical research, with the hopes of significantly impacting the diagnosis and treatment of diseases. Microarrays have the ability to measure the expression levels of thousands of genes simultaneously. This research entails a rigorous and systematic comparison of statistical classifiers composed of supervised learning methods and either univariate or genetic algorithm-based multivariate feature subset selection (FSS) techniques, all applied to six published two-class microarray datasets. These analyses should provide insights into univariate vs.

multivariate FSS and how to obtain realistic and honest misclassification error rates via cross-validation (CV). Ultimately, this research puts to test the more traditional implementations of CV and FSS and provides a solid foundation on which these topics can and should be further investigated when performing limited-sample classification using high-dimensional gene expression data. The analyses presented should be especially useful to researchers in the fields of bioinformatics, supervised learning, and variable selection, as well as anyone interested in microarray analysis.

Automated facial expression recognition is still a grand challenge in computer vision. The process of facial expression recognition consists of three main stages after image pre-processing and face acquisition. These stages are extracting the feature vector, which describes the facial image, reducing the dimensionality of that vector if possible, and finally, classifying test images into certain classes after training using the reduced dimensionality vector.

A contribution to the first stage is chosen since the key issue in the analysis is finding a descriptive, representative, efficient, and easy to compute feature vector of the facial image. This work lays the ground for building a system that automatically, and in real-time classifies mouth action units (of the facial action coding system) with acceptable rates, and that is illumination tolerant. This is done by proposing the application of local binary patterns (LBPs) to the feature extraction stage of mouth action unit classification. LBPs require no manual pre-processing or initialization, contribute to a real-time system by being computationally simple, are illumination tolerant, and can be used in low-resolution images.

The infinite number of websites in the internet world can be classified into a number of categories depending on the purpose for classification.In educational institutions, it is required that the internet facility be used for academic purposes only. This can happen through accurate classification of network traffic into two classes, educational websites and non-educational websites. In educational institutes, for the optimum use of network resources the use of non-educational websites should be banned.

In our research work, we explore the use of ML (machine learning) algorithms for classification of internet traffic into two classes. We discuss the basis for differentiating the traffic into these two classes, and investigate the impact of flexibility in making such differentiation on the performance of selected five ML algorithms. We also investigate the impact of class error in data set and propose a generalized feature subset which is robust against all these impacts. Results show that Bayes Net algorithm performs consistently for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision.

First published in 1983. This monograph is a review of the evolution of George Briggs’ informationprocessing model from a general schema beginning with the work of Saul Sternberg (1969a) and Edward E. Smith (1968) to a fairly well-detailed schematic representation of central processes that Briggs was working on at the time of his early death.