An advantage of rule induction over other machine learning algorithms is the comprehensibility of the models, a requirement for many data mining applications. However, many real life machine learning applications involve the ranking of cases and classification rules are not a good representation for this. There have been numerous studies to incorporate ranking capability into decision trees, bu...

Most of existing work on sports video analysis concentrates on highlight extraction. Few efforts devoted to the important issue as how to organize the extracted highlights which is adapt for the user preference. In this paper, we propose a novel approach to rank the highlights extracted from broadcast tennis video based on multi-modality analysis and relevance feedback. Firstly, visual and audi...

Learning to rank is an important area at the interface of machine learning, information retrieval and Web search. The technology has been successfully applied to web search, and is becoming one of the key machines for building search engines. The central challenge in optimizing various measures of ranking loss is that the objectives tend to be non-convex and discontinuous. In recent years, boos...

We address the problem of analyzing multiple related opinions in a text. For instance, in a restaurant review such opinions may include food, ambience and service. We formulate this task as a multiple aspect ranking problem, where the goal is to produce a set of numerical scores, one for each aspect. We present an algorithm that jointly learns ranking models for individual aspects by modeling t...

Review Opinion Diversification (RevOpiD) 2017 is a shared task which is held in International Joint Conference on Natural Language Processing (IJCNLP). The shared task aims at selecting top-k reviews, as a summary, from a set of re-views. There are three subtasks in RevOpiD: helpfulness ranking, representativeness ranking, and exhaustive coverage ranking. This year, our team submitted runs by t...

This paper proposes improvements and revisions to a recent approach in a voting system, and provides an effective approach with a stronger discriminate power. For this purpose, the advantage of a linear transformation is utilized to redene a previously used concept of virtual worst candidate, by incorporating the existing weight restrictions. Then, the best score of this virtual candidate is us...

One of the major problems in Data Envelopment Analysis (DEA) is to determine the projection of inefficient Decision Making Units (DMUs) into the efficient frontier. In conventional DEA models, inputs and outputs of inefficient DMUs alter arbitrarily for reaching to the efficient frontier. Nevertheless, sometimes the ability of DMUs is defined and restricted. Moreover, there are situations in th...

Ranking is a central problem in information retrieval. Much work has been done in the recent years to automate the development of ranking models by means of supervised machine learning. Feature selection aims to provide sparse models which are computationally efficient to evaluate, and have good ranking performance. We propose integrating the feature selection as part of the training process fo...

Ranking is a central problem in information retrieval. Much work has been done in the recent years to automate the development of ranking models by means of supervised machine learning. Feature selection aims to provide sparse models which are computationally efficient to evaluate, and have good ranking performance. We propose integrating the feature selection as part of the training process fo...

Service ranking has been long-acknowledged to play a fundamental role in helping users to select the best offerings among services retrieved from a search request. There exist many ranking mechanisms, each one providing ad hoc preference models that offer different levels of expressiveness. Consequently, applying a single mechanism to a particular scenario constrains the user to define preferen...