In this paper, we propose an online learning algorithm PRIL for learning ranking classifiers using interval labeled data and show its correctness. We show its convergence in finite number of steps if there exists an ideal classifier such that the rank given by it for an example always lies in its label interval. We then generalize this mistake bound result for the general case. We also provide ...

We study the problem of finding the most uniform partition of the class label distribution on an interval. This problem occurs, e.g., in supervised discretization of continuous features, where evaluation heuristics need to find the location of the best place to split the current feature. The weighted average of empirical entropies of the interval label distributions is often used in this task. ...

In this study a new approach to rank exponential fuzzy numbers using  -cuts is established. The metric distance of the interval numbers is extended to exponential fuzzy numbers. By using the ranking of exponential fuzzy numbers and using  -cuts the critical path of a project network is solved and illustrated by numerical examples. Keywords: Exponential Fuzzy Numbers,  -cuts, Metric Dista...

This paper introduces the interval-PROMETHEE method to the investment decision making of value investing. Five performance criteria are used for measuring the growth potential of firms and are represented by interval numbers based on the real financial data. The interval-PROMETHEE method is applied to rank the 20 randomly selected stocks in Shanghai Stock Exchange. The portfolio with the top 5 ...

Interval data envelopment analysis (interval DEA) deals with the problem of efficiency assessment when inputs and/or outputs decision making units (DMUs) are given as interval data. This paper focuses on ranking DMUs First, we define extreme efficient units, super score, best and worst (inefficiency) frontiers in DEA context. Then, propose a novel method based lower upper scores unit under eval...

On account of the existence of uncertainty, DEA occasionally faces the situation of imprecise data, especially when a set of DMUs include missing data, ordinal data, interval data, stochastic data, or fuzzy data. Therefore, how to evaluate the efficiency of a set of DMUs in interval environments is a problem worth studying. In this paper, we discussed the new method for evaluation and ranking i...