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This paper investigates a variation to Adlassnig's fuzzy relation based model for medical diagnosis. The proposed model is an attempt to closely replicate a physician's perceptions of symptom-disease associations and his approximate-reasoning for diagnosis. For proof of principle, the algorithm is evaluated in two sample studies. First case study relates to selected cardiovascular diseases, wherein the required parameters are estimated by interviewing physicians, and an evaluation is performed on a dataset of 79 cases. In the second study, the algorithm is implemented using an alternative semiautomatic approach for a more complex problem of diagnosing common infectious diseases, wherein the parameters are derived from a dataset of 92 case records; for evaluation, jack-knife is performed along with a comparison with Independence Bayes, considered here as the reference standard. The proposed algorithm was found to be as accurate as Independence Bayes for diagnosing common infectious diseases from the small dataset. This result may indicate the utility of proposed algorithm to optimally model the diagnostic process for small datasets; especially, due to its computational simplicity. Further studies on a variety of datasets are needed to establish such a utility. [ABSTRACT FROM AUTHOR]

The ocean color inverse problem consists of determining the concentrations of optically active constituents, such as chlorophyll, suspended particulate matter and colored dissolved organic matter, from remotely sensed multispectral measurements of the reflected sunlight back-scattered by the water body. In this paper, we approach this regression problem by using an evolutionary multi-objective algorithm, namely the (2+2) Modified Pareto Archived Evolutionary Strategy ((2+2)M-PAES), to optimize Takagi-Sugeno type (TS-type) fuzzy rule-based systems (FRBSs). Accuracy and complexity are the two competitive objectives to be simultaneously optimized. TS-type FRBSs are implemented as an artificial neural network; by training the neural network, the parameters of the fuzzy model are adjusted. In this way, the evolutionary optimization coarsely identifies the structure of the TS-type FRBSs, while the corresponding neural networks finely tune their parameters. As a result, a set of TS-type FRBSs with different trade-offs between accuracy and complexity is provided at the end of the optimization process. We show the effectiveness of our approach by comparing our results with those obtained on the ocean color inverse problem by other techniques recently proposed in the literature. [ABSTRACT FROM AUTHOR]

The application of machine learning to intrusion detection has been researched for several decades, however, with varying degrees of success. This paper focuses on two common techniques: Multi Layer Perceptrons (MLPs) and Decision Trees (DTs). Previous research on these techniques has produced contradictory results concerning their ability to detect particular classes of intrusion. Some of these contradictions are argued to be a result of properties of the data set used for empirical study, the KDD Cup '99 data set, which poses several challenges to learning algorithms. One particular challenge is considered here, learning from imbalanced data, which is an intrinsic problem to intrusion detection. Empirical results show that both the DT and MLP trained with back propagation obtain very poor classification rates of the minor classes, particularly U2R (User to Root) intrusions; the MLP often being unable to detect this class. An evolutionary neural network is employed, in which several evaluation functions are examined. Two general fitness measures are used, which lead to similar behaviour to training an MLP with back propagation. However, when employing evaluation functions that calculate the fitness proportionally to the instances of each class, thereby avoiding a bias towards the major class(es) in the data set, significantly improved true positive rates are obtained whilst maintaining a low false positive rate. [ABSTRACT FROM AUTHOR]