Comprehensibility is very important for a machine learning technique to be used in computer-aided medical diagnosis. Since an artificial neural network ensemble is composed of multiple artificial neural networks, its comprehensibility is worse than that of a single artificial neural network. In this paper, C4.5 Rule-PANE which combines artificial neural network ensemble with rule induction by regarding the former as a pre-process of the latter, is proposed. At first, an artificial neural network ensemble is trained. Then, a new training data set is generated by feeding the feature vectors of the original training instances to the trained ensemble and replacing the expected class labels of the original training instances with the class labels output from the ensemble. Additional training data may also be appended by randomly generating feature vectors and combining them with their corresponding class labels output from the ensemble. Finally, a specific rule induction approach, i.e. C4.5 Rule, is used to learn rules from the new training data set. Case studies on diabetes, hepatitis, and breast cancer show that C4.5 Rule-PANE could generate rules with strong generalization ability, which profits from artificial neural network ensemble, and strong comprehensibility, which profits from rule induction.

Quantitative attributes are usually discretized in naive-Bayes learning. We prove a theorem that explains why discretization can be effective for naive-Bayes learning. The use of different discretization techniques can be expected to affect the classification bias and variance of generated naive-Bayes classifiers, effects we name discretization bias and variance. We argue that by properly managing discretization bias and variance, we can effectively reduce naive-Bayes classification error. In particular, we propose proportional k-interval discretization and equal size discretization, two efficient heuristic discretization methods that are able to effectively manage discretization bias and variance by tuning discretized interval size and interval number. We empirically evaluate our new techniques against five key discretization methods for naive-Bayes classifiers. The experimental results support our theoretical arguments by showing that naive-Bayes classifiers trained on data discretized by our new methods are able to achieve lower classification error than those trained on data discretized by alternative discretization methods.

Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy.

I would like to express sincere gratitude and thanks to my adviser, Dr. Chiranjib Bhat-tacharyya. With his interesting thoughts and ideas, inspiring ideals and friendly nature, he made sure I was filled with enthusiasm and interest to do research all through my PhD. He was always approachable and spent ample time with me and all my lab mem-bers for discussions, though he had a busy schedule. I also thank Prof. M. N. Murty, Dr. Samy Bengio (Google Labs, USA) and Prof. Aharon Ben-Tal (Technion, Israel) for their help and co-operation. I am greatly in debt to my parents, wife and other family members for supporting and encouraging me all through the PhD years. I thank all my lab members and friends, especially Karthik Raman, Sourangshu, Rashmin, Krishnan and Sivaramakrishnan, for their useful discussions and comments. I thank the Department of Science and Technology, India, for supporting me finan-cially during the PhD work. I would also like to take this opportunity to thank all the people who directly and indirectly helped in finishing my thesis. i Publications based on this Thesis

Machine-learned classifiers are important components of many data mining and knowledge discovery systems. In several application domains, an explanation of the classifier's reasoning is critical for the classifier's acceptance by the end-user. We describe a framework, ExplainD, for explaining decisions made by classifiers that use additive evidence. ExplainD applies to many widely used classifiers, including linear discriminants and many additive models. We demonstrate our ExplainD framework using implementations of nave Bayes, linear support vector machine, and logistic regression classifiers on example applications. ExplainD uses a simple graphical explanation of the classification process to provide visualizations of the classifier decisions, visualization of the evidence for those decisions, the capability to speculate on the effect of changes to the data, and the capability, wherever possible, to drill down and audit the source of the evidence. We demonstrate the effectiveness of ExplainD in the context of a deployed web-based system (Proteome Analyst) and using a downloadable Python-based implementation.

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii An automatically trained, statistically based, fuzzy inference system that functions as a classifier is produced. The hybrid system is designed specifically to be used as a decision support system. This hybrid system has several features which are of direct and immediate utility in the field of decision support, in-cluding a mechanism for the discovery of domain knowledge in the form of explanatory rules through the examination of training data; the evaluation of such rules using a simple probabilistic weighting mech-anism; the incorporation of input uncertainty using the vagueness abstraction of fuzzy systems; and the provision of a strong confidence measure to predict the probability of system failure. Analysis of the hybrid fuzzy system and its constituent parts allows commentary on the weighting scheme and performance of the “Pattern Discovery ” system on which it is based. Comparisons against other well known classifiers provide a benchmark of the performance of the

This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method. 1

Since the early 1990s, artificial neural networks play an increasing role in the development of new biomedical systems. In United States, over last decades, the granted biomedical patents that explicitly refer to artificial neural networks in their title, abstract or key references amount to about 50% of the total number of granted biomedical patents with a significant element of computational intelligence. Furthermore, the number of granted biomedical patents that explicitly involve artificial neural networks has quadrupled in recent four years. This observation greatly indicates the growing commercial interest in biomedical products involving artificial neural networks.

this paper we present an alternative logistic-driven grouping approach

this paper we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3603 patients with peripheral arterial vascular diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multidisciplinary units, and (ii) how well patients can be selected for treatment in the new units. Given a-priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper we describe the results of the above-proposed methodology for patients with peripheral arterial vascular diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all logistic variables, and another grouping is based on two variables, found by applying factor analysis. On the basis of the results we can conclude that the search for logistic homogenous groups has advantages over more traditional search for medically homogenous groups. 1.