A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Perceived fairness of pay among people with and without disabilities: a propensity score matched analysis of working Australians

OBJECTIVES: Equity and fairness at work are associated with a range of organizational and health outcomes. Past research suggests that workers with disabilities experience inequity in the workplace. It is difficult to conclude whether the presence of disability is the reason for perceived unfair treatment due to the possible confounding of effect estimates by other demographic or socioeconomic factors. METHODS: The data source was the Household, Income, and Labor Dynamics in Australia (HILDA) survey (2001-2012). Propensity for disability was calculated from logistic models including gender, age, education, country of birth, and father's occupational skill level as predictors. We then used nearest neighbor (on propensity score) matched analysis to match workers with disabilities to workers without disability. RESULTS: Results suggest that disability is independently associated with lower fairness of pay after controlling for confounding factors in the propensity score matched analysis; although results do suggest less than half a standard deviation difference, indicating small effects. Similar results were apparent in standard multivariable regression models and alternative propensity score analyses (stratification, covariate adjustment using the propensity score, and inverse probability of treatment weighting). CONCLUSIONS: Whilst neither multivariable regression nor propensity scores adjust for unmeasured confounding, and there remains the potential for other biases, similar results for the two methodological approaches to confounder adjustment provide some confidence of an independent association of disability with perceived unfairness of pay. Based on this, we suggest that the disparity in the perceived fairness of pay between people with and without disabilities may be explained by worse treatment of people with disabilities in the workplace.

Camera model identification with unknown models

Feature based camera model identification plays an important role for forensics investigations on images. The conventional feature based identification schemes suffer from the problem of unknown models, that is, some images are captured by the camera models previously unknown to the identification system. To address this problem, we propose a new scheme: Source Camera Identification with Unknown models (SCIU). It has the capability of identifying images of the unknown models as well as distinguishing images of the known models. The new SCIU scheme consists of three stages: 1) unknown detection; 2) unknown expansion; and 3) (K+1)-class classification. Unknown detection applies a k-nearest neighbours method to recognize a few sample images of unknown models from the unlabeled images. Unknown expansion further extends the set of unknown sample images using a self-training strategy. Then, we address a specific (K+1)-class classification, in which the sample images of unknown (1-class) and known models (K-class) are combined to train a classifier. In addition, we develop a parameter optimization method for unknown detection, and investigate the stopping criterion for unknown expansion. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed SCIU scheme. When unknown models present, the identification accuracy of SCIU is significantly better than the four state-of-art methods: 1) multi-class Support Vector Machine (SVM); 2) binary SVM; 3) combined classification framework; and 4) decision boundary carving.

Construction of k-Lipschitz triangular norms and conorms from empirical data

This paper examines the practical construction of k-Lipschitz triangular norms and conorms from empirical data. We apply a characterization of such functions based on k-convex additive generators and translate k-convexity of piecewise linear strictly decreasing functions into a simple set of linear inequalities on their coefficients. This is the basis of a simple linear spline-fitting algorithm, which guarantees k-Lipschitz property of the resulting triangular norms and conorms.

On the use of fuzzy rule interpolation techniques for monotonic multi-input fuzzy rule base models

Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc.

Enhancing the failure mode and effect analysis methodology with fuzzy inference techniques

Traditional Failure Mode and Effect Analysis (FMEA) adopts the Risk Priority Number (RPN) ranking model to evaluate failure risks, to rank failures, as well as to prioritize actions. Although this approach is simple, it suffers from several shortcomings. In this paper, we investigate a number of fuzzy inference techniques for determining the RPN scores, in an attempt to overcome the weaknesses associated with the traditional RPN model. The main objective is to examine the possibility of using fuzzy rule interpolation and reduction techniques to design new fuzzy RPN models. The performance of the fuzzy RPN models is evaluated using a real-world case study pertaining to the test handler process in a semiconductor manufacturing plant. The FMEA procedure for the test handler is performed, and a fuzzy RPN model is developed. In addition, improvement to the fuzzy RPN model is proposed by refining the weights of the fuzzy production rules, hence a new weighted fuzzy RPN model. The ability of the weighted fuzzy RPN model in failure risk evaluation with a reduced rule base is also demonstrated.

Neural network and interval type-2 fuzzy system for stock price forecasting

Stock price forecast has long been received special attention of investors and financial institutions. As stock prices are changeable over time and increasingly uncertain in modern financial markets, their forecasting becomes more important than ever before. A hybrid approach consisting of two components, a neural network and a fuzzy logic system, is proposed in this paper for stock price prediction. The first component of the hybrid, i.e. a feedforward neural network (FFNN), is used to select inputs that are highly relevant to the dependent variables. An interval type-2 fuzzy logic system (IT2 FLS) is employed as the second component of the hybrid forecasting method. The IT2 FLS’s parameters are initialized through deployment of the k-means clustering method and they are adjusted by the genetic algorithm. Experimental results demonstrate the efficiency of the FFNN input selection approach as it reduces the complexity and increase the accuracy of the forecasting models. In addition, IT2 FLS outperforms the widely used type-1 FLS and FFNN models in stock price forecasting. The combination of the FFNN and the IT2 FLS produces dominant forecasting accuracy compared to employing only the IT2 FLSs without the FFNN input selection.

A hybrid approach to fuzzy risk analysis in stock market

The analysis and prediction of stock market has always been well recognized as a difficult problem due to the level of uncertainty and the factors that affect the price. To tackle this challenge problem, this paper proposed a hybrid approach which mines the useful information utilizing grey system and fuzzy risk analysis in stock prices prediction. In this approach, we firstly provide a model which contains the fuzzy function, k-mean algorithm and grey system (shorted for FKG), then provide the model of fuzzy risk analysis (FRA). A practical example to describe the development of FKG and FRA in stock market is given, and the analytical results provide an evaluation of the method which shows promote results. © 2013 IEEE.

Adaptive translational cueing motion algorithm using fuzzy based tilt coordination

Driving simulators have become useful research tools for the institution and laboratories which are studying in different fields of vehicular and transport design to increase road safety. Although classical washout filters are broadly used because of their short processing time, simplicity and ease of adjust, they have some disadvantages such as generation of wrong sensation of motions, false cue motions, and also their tuning process which is focused on the worst case situations leading to a poor usage of the workspace. The aim of this study is to propose a new motion cueing algorithm that can accurately transform vehicle specific force into simulator platform motions at high fidelity within the simulator’s physical limitations. This method is proposed to compensate wrong cueing motion caused by saturation of tilt coordination rate limit using an adaptive correcting signal based on added fuzzy logic into translational channel to minimize the human sensation error and exploit the platform more efficiently.

Adaptive washout algorithm based fuzzy tuning for improving human perception

The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the simulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adaptive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of highpass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cueing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance, improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.