APPLICATION OF FUZZY-MLP MODEL TO ULTRASONIC LIVER IMAGE CLASSIFICATION

In this paper, we propose the application of fuzzy-MLP in theclassification of ultrasonic liver images. The four sets of ultrasonic liverimages used in the experiment are: normal, liver cysts, alcoholic cirrhosisand carcinoma.To deal with the sample images efficiently, we extract textural features fromthe Pathology Bearing Regions (PBRs) of the ultrasound liver images. Theselected features for the classification are entropy, energy and maximumprobability-based texture features extracted using gray level co-occurrencematrix second-order statistics. The fuzzy-MLP model is constructed for theselected features classify various categories of ultrasonic liver images.The efficacy of Fuzzy-MLP model and conventional artificial neural network(ANN) has been compared on the basis of the same feature vector. A testwith 82 training data and 110 test data for all the four classes shows 92.73%classification accuracy for the proposed fuzzy-MLP model. It is comparedwith the 81.82% counterpart provided by conventional ANN method

Interval based fuzzy systems for identification of important genes from microarray gene expression data: Application to carcinogenic development

AbstractIn the present article, we develop two interval based fuzzy systems for identification of some possible genes mediating the carcinogenic development in various tissues. The methodology involves dimensionality reduction, classifying the genes through incorporation of the notion of linguistic fuzzy sets low, medium and high, and finally selection of some possible genes mediating a particular disease, obtained by a rule generation/grouping technique. The effectiveness of the proposed methodology, is demonstrated using five microarray gene expression datasets dealing with human lung, colon, sarcoma, breast cancer and leukemia. Moreover, the superior capability of the methodology in selecting important genes, over five other existing gene selection methods, viz., Significance Analysis of Microarrays (SAM), Signal-to-Noise Ratio (SNR), Neighborhood analysis (NA), Bayesian Regularization (BR) and Data-adaptive (DA) is demonstrated, in terms of the enrichment of each GO category of the important genes based on P-values. The results are appropriately validated by earlier investigations, gene expression profiles and t-test. The proposed methodology has been able to select genes that are more biologically significant in mediating the development of a disease than those obtained by the others

A proposal on reasoning methods in fuzzy rule-based classification systems

AbstractFuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism to the set of rules. Finally, to show the increase of the system generalization capability provided by the proposed FRMs, we point out some results obtained by their integration in a fuzzy rule generation process

A proposal on reasoning methods in fuzzy rule-based classification systems

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism to the set of rules. Finally, to show the increase of the system generalization capability provided by the proposed FRMs, we point out some results obtained by their integration in a fuzzy rule generation process.CICYT TIC96-077

Fuzzy multi-layer perceptron, inferencing and rule generation

A connectionist expert system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposed. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty expressing confidence in the decision. The model is capable of querying the user for the more important input feature information, if and when required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The magnitudes of the connection weights of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory rules are provided in natural forms. The effectiveness of the algorithm is tested on the speech recognition problem, on some medical data and on artificially generated intractable (linearly nonseparable) pattern classes