Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. In this paper, with the help of several counter examples it is proved that ranking method proposed by Chen and Chen (Expert Syst Appl 36:6833–6842, 2009) is incorrect. The main aim of this paper is to propose a new approac...

Label ranking is the task of inferring a total order over a predefined set of labels for each given instance. We present a general framework for batch learning of label ranking functions from supervised data. We assume that each instance in the training data is associated with a list of preferences over the label-set, however we do not assume that this list is either complete or consistent. Thi...

The ubiquity of the multimedia has raised a need for the system that can store, manage, structured the multimedia data in such a way that it can be retrieved intelligently. One of the current issues in media management or data mining research is ranking of retrieved documents. Ranking is one of the provocative problems for information retrieval systems. Given a user query comes up with the mill...

Rough set theory is used in data mining through complex learning systems and uncertain information decision from artificial intelligence. For multiple attribute decision making, rough sets employ attribute reduction to generate decisive rules. However, dynamic information databases, which record attribute values changing with time, raise questions to rough set based multiple attribute reduction...

In the design of human-computer ranking systems for the adaptive display of information, designers often define a domain-specific scoring function which maps items such as people or information search results to numeric scores. Classic ranking systems typically display these items in a linear fashion, sorted by score. There are shortcomings to this approach: such ranking systems do not provide ...

This paper is concerned with relevance ranking in search, particularly that using term dependency information. It proposes a novel and unified approach to relevance ranking using the kernel technique in statistical learning. In the approach, the general ranking model is defined as a kernel function of query and document representations. A number of kernel functions are proposed as specific rank...

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we p...

Ranking is a core problem for information retrieval since the performance of the search system is directly impacted by the accuracy of ranking results. Ranking model construction has been the focus of both the fields of information retrieval and machine learning, and learning to rank in particular has attracted much interest. Many ranking models have been proposed, for example, RankSVM is a sta...