The ability to intelligently collect, manage and analyze information about customers and sellers is a key source of competitive advantage for an e-business. This ability provides an opportunity to deliver real time marketing or services that strengthen customer relationships. This also enables an organization to gather business intelligence about a customer that can be used for future planning ...

BACKGROUND One of people's major motives for going online is the search for health-related information. Most consumers start their search with a general search engine but are unaware of the fact that its sorting and ranking criteria do not mirror information quality. This misconception can lead to distorted search outcomes, especially when the information processing is characterized by heuristi...

The quality of a learning algorithm is characterized by the accuracy, stability and comprehensibility of the models it generates. Though ensembles produce accurate and stable classifiers, they are hard to interpret. In this paper, we propose a meta-learning method for ID3 that makes use of an ensemble for gaining accuracy and stability and yet produces a single comprehensible classifier. The ma...

Formal concept analysis is a method of exploratory data analysis that aims at the extraction of natural clusters from object–attribute data tables. The clusters, called formal concepts, are naturally interpreted as human-perceived concepts in a traditional sense and can be partially ordered by a subconcept–superconcept hierarchy. The hierarchical structure of formal concepts (so-called concept ...

Europe’s capacity to explore the envisaged pathways that achieve its near- and long-term energy climate objectives needs be significantly enhanced. In this perspective, we discuss how is supported by climate-economy models, international modelling teams are organised within structured communication channels consortia as well coordinate multi-model analyses provide robust scientific evidence. No...

Abstract With the rapid growth of machine learning, deep neural networks (DNNs) are now being used in numerous domains. Unfortunately, DNNs “black-boxes”, and cannot be interpreted by humans, which is a substantial concern safety-critical systems. To mitigate this issue, researchers have begun working on explainable AI (XAI) methods, can identify subset input features that cause DNN’s decision ...

The main objective of this paper is to compare the classification accuracy provided large, comprehensive collections of patterns (rules) derived from archives of past observations, with that provided by small, comprehensible collections of patterns. This comparison is carried out here on the basis of an empirical study, using several publicly available datasets. The results of this study show t...

Certain texts, such as clinical reports and clinical trial records, are written by professionals for professionals while being increasingly accessed by lay people. To improve the comprehensibility of such documents to the lay audience, we conducted a pilot study to analyze terms used primarily by health professionals, and explore ways to make them more comprehensible to lay people.

Although they are applicable to a wide array of problems, and have demonstrated good performance on a number of diicult, real-world tasks, neural networks are not usually applied to problems in which compre-hensibility of the acquired concepts is important. The concept representations formed by neural networks are hard to understand because they typically involve distributed, nonlinear relation...

This paper presents a new approach to acquisition of comprehensible fuzzy rules for fuzzy modeling from data using evolutionary programming(EP). For the accuracy of the model, it is e ective to allow overlapping of membership functions with each other in the fuzzy model. From the viewpoint of knowledge acquisition, it is desirable that the model has a smaller number of membership functions with...