An Interval Type-2 Fuzzy Set Approach to Breast Cancer Dataset Analysis

The analysis of medical data is frequently characterized with uncertainties which tend to attract complexity. Therefore in this paper, an Interval Type-2 fuzzy set model: Hao and Mendel Approach (HMA) is proposed to fuzzify breast cancer data in order to handle quantitative attribute sharp boundary problem and resolve inter and intra uncertainties. The HMA comprises of the data and the fuzzy part to create interval type-2 fuzzy values. The data part involves data preprocessing of the experts’ intervals and the fuzzy set part establishes the structure of the FOU. The type reduction of the aggregated FOU is achieved by computing the centroid (measure of uncertainty) of the Fuzzy Set using the Enhanced Kernik-Mendel (EKM) approach. The defuzzification of the outcome which is an interval Type-2 Fuzzy set is achieved by computing the average of the interval’s two endpoints; this captures and reflects the aggregate uncertainty of all the medical experts for breast cancer analysis. This will enhance interpretability of discrete intervals in medical dataset, providing a smooth transition from a fuzzy set to another in order to handle the sharp boundary interval problem and cater for inter and intra uncertainty in data interval value as the same word has diverse connotations to different people

Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation

© 2014 Elsevier B.V. All rights reserved. This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence resulting in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria movement affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while de.ning a small step-size gives high accuracy but produces slower convergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as parametric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator

Genetically evolved fuzzy rule-based classifiers and application to automotive classification

10.1007/978-3-540-89694-4_11Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5361 LNAI101-11