Uncertainty coefficient

Abstract: Uncertainty measures can supply new points of view for analyzing data and help us to disclose the substantive characteristics of data sets. Some uncertainty measures for single-valued information systems or single-valued decision systems have been developed. However, there are few studies on the uncertainty measurement for interval-valued information systems or interval-valued decision systems. This paper addresses the uncertainty measurement problem in interval-valued decision systems. An extended conditional entropy is proposed in interval-valued decision systems based on possible degree between interval values. Consequently, a concept called rough decision entropy is introduced to evaluate the uncertainty of an interval-valued decision system. Besides, the original approximation accuracy measure proposed by Pawlak is extended to deal with interval-valued decision systems and the concept of interval approximation roughness is presented. Experimental results demonstrate that the rough decision entropy measure and the interval approximation roughness measure are effective and valid for evaluating the uncertainty measurement of interval-valued decision systems. Experimental results also indicate that the rough decision entropy measure outperforms the interval approximation roughness measure.

Abstract: Most traditional assessment methods, which have complicated mathematic formulas, are difficult for calculation and application in water resources system assessment. A new approach to water resources system assessment, the set pair analysis method (SPAM), has been proposed based on the principle of set pair analysis (SPA). The basic ideals and steps of SPAM are discussed. The proposed method can take fuzzy property of threshold values for grade standards into full account and avoid determining the discrepancy uncertainty coefficient i or i1, i2, i3, ... in SPA. The presented method is simple in concept, convenient to calculate and feasible for application. Two case studies of water resources assessment have been made. The results show that the proposed method is satisfactory.

Abstract: The paper introduces a two-step method of quantifying eye movement transitions between Areas of Interests (AOIs). First, individuals' gaze switching patterns, represented by fixated AOI sequences, are modeled as Markov chains. Second, Shannon's entropy coefficient of the fit Markov model is computed to quantify the complexity of individual switching patterns. To determine the overall distribution of attention over AOIs, the entropy coefficient of individuals' stationary distribution of fixations is calculated.The novelty of the method is that it captures the variability of individual differences in eye movement characteristics, which are then summarized statistically. The method is demonstrated on gaze data collected during free viewing of classical art paintings. Shannon's coefficient derived from individual transition matrices is significantly related to participants' individual differences as well as to their aesthetic experience of art pieces.

Abstract: Multi-objective flow shop scheduling problem with sequence-dependent setup times (MOFSP-SDST) is a class of important production scheduling problem with strong industry background. In this paper, a MOFSP-SDST mathematic model with the objectives of total production cost, makespan, mean flow time and mean idle time of machines is developed. To solve this multi-objective model, a novel multi-objective approach based on fuzzy correlation entropy analysis is proposed firstly. In this multi-objective approach, two types of objective function value sequences, namely the referenced function value sequence and comparable function value sequence, are constructed and mapped into two types of fuzzy sets by a modified relative membership function. The fuzzy correlation entropy coefficient between the two types of fuzzy sets is used to select better solutions in a multi-objective problem. A discrete multi-objective fireworks algorithm (DMOFWA) is proposed to address the MOFSP-SDST. In the DMOFWA, a new multi-objective approach is adopted to handle the multiple objectives and guide the search of the algorithm. Two kinds of machine learning strategies are adopted, namely opposition-based learning (OBL) and clustering analysis (CA). The OBL is employed to learn from the current search space and improve the exploration ability of DMOFWA, and the CA based on fuzzy correlation entropy coefficient is proposed to cluster firework individuals. Computational and statistical results show that the novel multi-objective approach, OBL and CA strategies can effectively improve the performance of DMOFWA. Furthermore, the results indicate that DMOFWA performs better than four state-of-the-art comparison algorithms.