Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models

The continued reliance on machine learning algorithms and robotic devices in the medical and engineering practices has prompted the need for the accuracy prediction of such devices. It has attracted many researchers in recent years and has led to the development of various ensembles and standalone models to address prediction accuracy issues. This study was carried out to investigate the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. In this study we proposed a model termed EKF-RBFN-ADABOOST

Correlated variability of the reflection fraction with the X-ray flux and spectral index for Mkn 478

The X-ray spectrum of Mkn 478 is known to be dominated by a strong soft excess which can be described using relativistic blurred reflection. Using observations from {\it XMM-Newton}, {\it AstroSat} and {\it Swift}, we show that for the long-term ($\sim$ years) and intermediate-term (days to months) variability, the reflection fraction is anti-correlated with the flux and spectral index, which implies that the variability is due to the hard X-ray producing corona moving closer to and further from the black hole. Using flux-resolved spectroscopy of the {\it XMM-Newton} data, we show that the reflection fraction has the same behaviour with flux and index on short time-scales of hours. The results indicate that both the long-term and short-term variability of the source is determined by the same physical mechanism of strong gravitational light bending causing enhanced reflection and low flux as the corona moves closer to the black hole.Comment: Accepted for Publication in MNRAS, 8 pages, 8 figures, 5 Table

Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods

This paper presents the empirical comparison of boosting implementation by reweighting and resampling methods. The goal of this paper is to determine which of the two methods performs better. In the study, we used four algorithms namely: Decision Stump, Neural Network, Random Forest and Support Vector Machine as base classifiers and AdaBoost as a technique to develop various ensemble models. We applied 10-fold cross validation method in measuring and evaluating the performance metrics of the models. The results show that in both methods the average of the correctly classified and incorrectly classified are relatively the same. However, average values of the RMSE in both methods are insignificantly different. The results further show that the two methods are independent of the datasets and the base classier used. Additionally, we found that the complexity of the chosen ensemble technique and boosting method does not necessarily lead to better performance

Prediction of Breast Cancer Survivability using Ensemble Algorithms

In this paper we propose new ensemble cancer survivability prediction models based three variants of AdaBoost algorithm to extend the application range of ensemble methods. In our approach to address the problem of low efficiency and slow speed we use Random Forest, Radial Basis Function and Neural Network algorithms as base learners and AdaBoostM1, Real AdaBoost and MultiBoostAB as ensemble techniques. AdaBoost is a technique that iteratively trains its base classifiers to generate committee of strong classifiers to improve their performance and prediction accuracy. There has been major research in ensemble modeling in statistics, medicine, technology and artificial intelligence in the last three decades. This might be because of the effectiveness and reliability of the technique in helping medical and other professionals in diagnosis and incident predictions. However, there is a need to improve the accuracy of the existing models address current challenges. In this paper we use state of the art Wisconsin breast cancer dataset in training and testing the proposed hybrid models. The performance of the models was evaluated using the following performance metrics: Accuracy, RMSE, TP Rate, FP Rate, Precision and ROC Area. The results of our study shows that MBAB-RF and AdaM1-RF models have the same accuracy prediction of 97% and RA + ANN has the worst prediction accuracy of 88%. Additionally we found that all ANN models requires more time to train its committee of classifiers compared to RFB models that requires the least time despite the fact that RBF is a family of ANN algorithm

A Search for X-ray/UV Correlation in the Reflection-Dominated Seyfert 1 Galaxy Mrk 1044

Correlated variability between coronal X-rays and disc optical/UV photons provides a very useful diagnostic of the interplay between the different regions around an active galactic nucleus (AGN) and how they interact. AGN that reveal strong X-ray reflection in their spectra should normally exhibit optical/UV to X-ray correlation consistent with reprocessing -- where the optical/UV emission lag behind the X-rays. While such correlated delay has been seen in some sources, it has been absent in others. \rm{Mrk~1044} is one such source that has been known to reveal strong X-ray reflection in its spectra. In our analysis of three long \textit{XMM-Newton} and several \textit{Swift} observations of the source, we found no strong evidence for correlation between its UV and X-ray lightcurves both on short and long time scales. Among other plausible causes for the non-detection, we posit that higher X-ray variability than UV and strong general relativistic effects close to the black hole may also be responsible. We also present results from the spectral analysis based on \textit{XMM-Newton} and \textit{NuSTAR} observations, which show the strong soft X-ray excess and iron K$\alpha$ line in the 0.3--50 keV spectrum that can be described by relativistic reflection.Comment: Accepted for Publication in APJ, 13 pages, 11 figures, 2 table

MEAT QUALITY AND LIPID PROFILE OF BROILER CHICKENS FED DIETS CONTAINING TURMERIC (CURCUMA LONGA) POWDER AND CAYENNE PEPPER (CAPSICUM FRUTESCENS) POWDER AS ANTIOXIDANTS

This experiment was designed to determine the meat quality and lipid profile of broiler chickens fed diets containing turmeric (Curcuma longa) powder (Tur) and cayenne pepper (Capsicum frutescens) powder (Cay) as antioxidants. Two hundred and forty three (two-week old) Abor Acre broiler chicks were randomly allotted to nine treatment groups of 27 birds each, consisting of three replicates of nine birds each in a completely randomised design. Three levels of Tur (0, 2 and 4 g/kg) and three levels of Cay (0, 1 and 2 g/kg) were used to provide nine dietary treatments.  Meat quality indices such as cook and refrigerated losses, water absorptive power, etc were measured and determined at the 8th week. Broiler Chickens fed the basal diet had highest meat dry matter, protein content and least (p<0.05) meat pH, cook and refrigeration loss values. Meat triglyceride and meat malondialdehyde value was best (p<0.05) in treatments fed dietary 2 g/kg Cay, while chickens fed 2 g/kg Cay, 2 g/kg Tur + 1 g/kg Cay and 2 g/kg Tur + 2 g/kg Cay had better meat lipoprotein values. For meat sensory characteristic, meat flavour of broiler chickens fed diets containing 2 and 4 g/kg dietary Tur, were  moderately liked while overall flavour was best (p<0.05) in groups fed the basal diet with no dietary additive. It was evident in the study that the dietary inclusions of the test ingredients limited lipid oxidation, thus improved storage duration and meat flavor.   &nbsp