Optimal Thresholds for Classification Trees using Nonparametric Predictive Inference

In data mining, classification is used to assign a new observation to one of a set of predefined classes based on the attributes of the observation. Classification trees are one of the most commonly used methods in the area of classification because their rules are easy to understand and interpret. Classification trees are constructed recursively by a top-down scheme using repeated splits of the training data set, which is a subset of the data. When the data set involves a continuous-valued attribute, there is a need to select an appropriate threshold value to determine the classes and split the data. In recent years, Nonparametric Predictive Inference (NPI) has been introduced for selecting optimal thresholds for two- and three-class classification problems, where the inferences are explicitly in terms of a given number of future observations and target proportions. These target proportions enable one to choose weights that reflect the relative importance of one class over another. The NPI-based threshold selection method has previously been implemented in the context of Receiver Operating Characteristic (ROC) analysis, but not for building classification trees. Due to the predictive nature of the NPI-based threshold selection method, it is well suited for the classification tree method, as the end goal of building classification trees is to use them for prediction as well. In this thesis, we present new classification algorithms for building classification trees using the NPI approach for selecting the optimal thresholds. We first present a new classification algorithm, which we call the NPI2-Tree algorithm, for building binary classification trees; we then extend it to build classification trees with three ordered classes, which we call the NPI3-Tree algorithm. In order to build classification trees using our algorithms, we introduce a new procedure for selecting the optimal values of target proportions by optimising classification performance on test data. We use different measures to evaluate and compare the performance of the NPI2-Tree and the NPI3-Tree classification algorithms with other classification algorithms from the literature. The experimental results show that our classification algorithms perform well compared to other algorithms. Finally, we present applications of the NPI2-Tree and NPI3-Tree classification algorithms on noisy data sets. Noise refers to situations that occur when the data sets used for classification tasks have incorrect values in the attribute variables or the class variable. The performances of the NPI2-Tree and NPI3-Tree classification algorithms in the case of noisy data are evaluated using different levels of noise added to the class variable. The results show that our classification algorithms perform well in case of noisy data and tend to be quite robust for most noise levels, compared to other classification algorithms

State formation and identity in the middle East and North Africa

© Kenneth Christie and Mohammad Masad 2013. For states in the Middle East, North Africa, and South Asia, the Arab Spring has had different implications and consequences, stemming from the politics of identity and the historical and political processes that have shaped development. This book focuses on how these factors interact with globalization and affect state formation

A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time

Development of an estimative model for the optimal tack coat dosage based on aggregate gradation of hot mix asphalt pavements

In this work the performance of tack coats on asphalt pavement layers is analysed. Adjustment models based on experimental measurements were implemented, relating surface layer macro-texture and aggregate content larger than 8 mm. The best fits were obtained with a Gompertz model, which follows the expected physical macro-texture changes outside the test range. Shear strength was analysed, through prediction curves of each evaluated tack coat dosage, with an optimum tack coat performance for aggregate contents larger than 8 mm between 45% and 50%, and no relevant influence of the tack coat dosage used.The authors would like to acknowledge the support provided by the Technologic Research Construction Group (GITECO) and the Group of Roads of Santander at Cantabria University for the development of tests and samples. We would also like to thank the company Emilio Bolado S.L. and the Society for the Development of Cantabria Region (SODERCAN) for the material provided, and the DID Research Department from the Austral University of Chile for the support

The Medieval Islamic Apocalyptic Tradition: Divination, Prophecy and the End of Time in the 13th Century Eastern Mediterranean

This study is part of a growing research focus on Islamic apocalypticism and divination in medieval Islamic culture. Though some work is being done on magic ( sihr ) and astrology ( tanjîm ), there is less scholarly interest in Islamic apocalyptic writings, including prophetic traditions ( hadîth ) on the end of time ( qiyâma ) and its tribulations ( fitan wa-malâhim ) and various forms of prophecies, numerology and divination, such as hurûf and jafr wa-jâmi\u27a . Such scarcity of interest reflects the standard scholarly view that medieval Islamic culture was dominated by Sunni orthodoxy and had little tolerance for Shi\u27a sympathies, divination, and the occult. This view is hardly supported by historical evidence. Medieval Islamic literature points to a distinct interest in divination, occultism, and apocalyptic prognostication. The study uses unpublished and primary sources to recover the Islamic apocalyptic tradition of the 7 th /13 th century Eastern Mediterranean. In addition to a lengthy introduction and conclusion, the study has four main chapters: a literature review; an overview of the life of the medieval scholar Ibn Talha and his prophecy, al-Durr al-munazzam ; an account of the special role of the House of the Prophet ( Ahl al-Bayt ) in Islamic divination and apocalyptic visions; and finally, a brief review of the lives of three scholars, two Sunni and one Shi\u27i, namely al-Ganjî, al-Sulamî, and al-Irbilî, who can be seen as examples of the continued appeal of divination and the complexity of medieval Islamic religiosity. A critical examination of the authors and texts considered in this study reveals two important findings: first, that divinatory and apocalyptic activities had a significant place in medieval Islamic culture; and secondly, that some respected Sunni scholars had such a strong loyalty to the \u27Alîds and the Imâms that they are hardly distinguishable from their Shi\u27i counterparts. This brings into question the conventional view that sees medieval Islamic culture mainly in terms of a triumph of an orthodox form of Sunnism. Overall, this dissertation is meant as a road map, highlighting an otherwise marginalized cultural milieu to help offer a fresh look and a paradigm shift in the study of medieval Islamic divination and apocalyptic literature in the Levant

Development of Test Methods to Evaluate the Printability of Concrete Materials for Additive Manufacturing

This study proposes test methods for assessing the printability of concrete materials for Additive Manufacturing. The printability of concrete is divided into three main aspects: flowability, setting time, and buildability. These properties are considered to monitor the critical quality of 3DCP and to ensure a successful print. Flowability is evaluated through a rheometer test, where the evolution of shear yield strength is monitored at a constant rate (rpm), similar to the printer setup. Flowability limits were set based on the user-defined maximum thickness of a printed layer and the onset of gaps/cracks during printing. Setting time is evaluated through an ultrasonic wave pulse velocity test (UPV), where the first inflection point of the evolution of the UPV graph corresponds to the setting time of the concrete specimen. The results from this continuous non-destructive test were found to correlate with the results from the discrete destructive ASTM C-191 test for measuring setting time with a maximum difference of 5% between both sets of values. Lastly, buildability was evaluated through the measurement of the early-age compressive strength of concrete, and a correlation with the UPV results obtained a predictive model that can be used in real-time to non-destructively assess the material buildability. This predictive model had a maximum percentage difference of 13% with the measured values. The outcome of this study is a set of tests to evaluate the properties of 3D printable concrete (3DP) material and provide a basis for a framework to benchmark and design materials for additive manufacturing

Properties and Microstructure Distribution of High-Performance Thermal Insulation Concrete

The aim of this experimental study is to develop high strength and lightweight concrete mixture suitable for structural applications. This work investigates the effect of replacing normal aggregate either partially or totally with expanded perlite aggregate. This material allows for better thermal insulation properties, thus decreasing the energy usage within the life cycle of the concrete structure. Expanded perlite aggregate was used in concrete by 20%, 40%, 60%, 80%, and 100% in replacement of the natural aggregate. Material characterization tests of compressive strength, flexural strength, and thermal conductivity were carried out for six concrete mixtures. In addition, microstructure analysis was performed with the aid of a micro-computed tomography system to investigate the effects and relation of microstructure quantities on material properties. The proposed concrete mixture, which has 100% of expanded perlite aggregate, has a unit weight of 1703 kg/m3 and achieved reduction percentage of thermal conductivity around 62% (1.81 to 0.69 W·m−1·K−1) and a compressive strength of 42 MPa at 28 days; and thus is ideal for structural applications with enhanced properties