An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network)

Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System

Heart disease is any disease that affects the normal condition and functionality of heart. Coronary Artery Disease (CAD) is the most common. It is caused by the accumulation of plaques within the walls of the coronary arteries that supply blood to the heart muscles. It may lead to continued temporary oxygen deprivation that will result in the damage of heart muscles. CAD caused more than 7,000,000 deaths every year in the worldwide. It is the second cause of death in Malaysia and the major cause of death in the world. To diagnose CAD, cardiologists usually perform many diagnostic steps. Unfortunately, the results of the diagnostic tests are difficult to interpret which do not always provide defmite answer, but may lead to different opinion. To help cardiologists providing correct diagnosis of CAD in less expensive and non- invasive manner, many researchers had developed decision support system to diagnose CAD. A fuzzy decision support system for the diagnosis of coronary artery disease based on rough set theory is proposed in this thesis. The objective is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. This proposed system is based on evidences or raw medical data sets, which are taken from University California Irvine (UCI) database. The proposed system is designed to be able to handle the uncertainty, incompleteness and heterogeneity of data sets. Artificial Neural Network with Rough Set Theory attribute reduction (ANNRST) is proposed is the imputation method to solve the incompleteness of data sets. Evaluations of ANNRST based on classifiers performance and rule filtering are proposed by comparing ANNRST and other methods using classifiers and during rule filtering process. RST rule inq'u ction is applied to ANNRST imputed data sets. Numerical values are discretized using Boolean reasoning method. Rule selection based on quality and importance is proposed. RST rule importance measure is used to select the most important high quality rules. The selected rules are used to build fuzzy decision support systems. Fuzzification based on discretization cuts and fuzzy rule weighing based on rule quality are proposed. Mamdani inference method is used to provide the decision with centroid defuziification to give numerical results, which represent the possibility of blocking in coronary, arteries. The results show that proposed ANNRST has similar performance to ANN and outperforms k-Nearest Neighbour (k-NN) and Concept Most Common attribute valueFilling (CMCF). ANNRST is simpler than ANN because it has fewer input attributes and more suitable to be applied for missing data imputation problem. ANNRST also provides strong relationship between original and imputed data sets. It is shown that ANNRST provide better RST rule based classifier than CMCF and k-NN during rule filtering process. Proposed RST based rule selection also performs better than other filtering methods. Developed Fuzzy Decision Support System (FOSS) provides better performance compared to multi layer perceptron ANN, k-NN, rule induction method called C4.5 and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) applied on UCI CAD data sets and Ipoh Specialist Hospital's patients. FOSS has transparent knowledge representation, heterogeneous and incomplete input data handling capability. FOSS is able to give the approximate percentage of blocking of coronary artery based on 13 standard attributes based on historical, simple blood test and ECG data, etc, where coronary angiography or cardiologist can not give the percentage. The results of FOSS were evaluated by three local cardiologists and considered to be efficient and useful

Vision-based neural network classifiers and their applications

A thesis submitted for the degree of Doctor of Philosophy of University of LutonVisual inspection of defects is an important part of quality assurance in many fields of production. It plays a very useful role in industrial applications in order to relieve human inspectors and improve the inspection accuracy and hence increasing productivity. Research has previously been done in defect classification of wood veneers using techniques such as neural networks, and a certain degree of success has been achieved. However, to improve results in tenus of both classification accuracy and running time are necessary if the techniques are to be widely adopted in industry, which has motivated this research. This research presents a method using rough sets based neural network with fuzzy input (RNNFI). Variable precision rough set (VPRS) method is proposed to remove redundant features utilising the characteristics of VPRS for data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable foml for input to an improved BP neural network classifier. The improved BP neural network classifier is improved in three aspects: additional momentum, self-adaptive learning rates and dynamic error segmenting. Finally, to further consummate the classifier, a uniform design CUD) approach is introduced to optimise the key parameters because UD can generate a minimal set of uniform and representative design points scattered within the experiment domain. Optimal factor settings are achieved using a response surface (RSM) model and the nonlinear quadratic programming algorithm (NLPQL). Experiments have shown that the hybrid method is capable of classifying the defects of wood veneers with a fast convergence speed and high classification accuracy, comparing with other methods such as a neural network with fuzzy input and a rough sets based neural network. The research has demonstrated a methodology for visual inspection of defects, especially for situations where there is a large amount of data and a fast running speed is required. It is expected that this method can be applied to automatic visual inspection for production lines of other products such as ceramic tiles and strip steel