A feature dependent Naive Bayes approach and its application to the software defect prediction problem

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task, which involves anatomy, artificial intelligence, and sensing and computing networks. A wearable wireless sensor network (WWSN) turns into an emerging technology, which is capable of acquiring dynamic data related to a human body's physiological conditions. The collected data can be applied to detect anomalies in a patient, so that he or she can receive an early alert about the adverse trend of the health condition, and doctors can take preventive actions accordingly. In this paper, a new WWSN for anomaly detections of health conditions has been proposed, system architecture and network has been discussed, the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN. The novelty of the detected model lies in its relevance to chronobiology. Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of t...

An emerging technology – wearable wireless sensor networks with applications in human health condition monitoring

This book contains substantially extended and revised versions of the best papers from the 15th International Conference on Enterprise Information Systems, ICEIS 2013, held in Angers, France, in July 2013.The 29 full and two invited papers included in this volume were carefully reviewed and selected from 321 submissions. They reflect state-of-the-art research focusing mainly on real-world applications and highlight the benefits of information systems and technology for industry and services, thus connecting academia with the world of real enterprises.The topics covered are: databases and information systems integration, artificial intelligence and decision support systems, information systems analysis and specification, software agents and Internet computing, humancomputer interaction, and enterprise architecture.

Enterprise Information Systems

Mining Blogs And Forums To Understand the Use of Social Media in Customer Co-creation

The high dimensional dataset results in more noise, will require more computations, has huge sparsity linked with high dimensional features and has thus introduced great challenges in data analysis. To efficiently manipulate and address the impact of the challenges faced by high dimensional dataset, researchers used several features reduction methods. Feature reduction is a formidable step, when dealing with improving the accuracy and reducing the processing time, within high-dimensional data; wherein the feature set is reduced before applying data mining or statistical methods. However with attribute reduction, there is a high chance of loss of important information. In order to avoid information loss, one way is to assign weights to the attributes through domain-expert which is a subjective exercise. It is not only costly but also requires human-expert of the field. Therefore, there is a need for a technique to automatically assign more appropriate weights without involving domain expert. This paper presents a novel features weighting technique. The technique employs a genetic algorithm (GA) to automatically assign weights to the attributes based on Naive Bayes (NB) classification. Experiments have been conducted on publically available dataset to compare the performance of the proposed approach and NB approach without the weighted features for predicting customer churn in telecommunication sector. The experimental results have demonstrated that the proposed technique outperformed through achieving an overall 89.1% accuracy, 95.65% precision which shows the effectiveness of the proposed technique.

Features Weight Estimation Using a Genetic Algorithm for Customer Churn Prediction in the Telecom Sector

Automated information retrieval is critical for enterprise information systems to acquire knowledge from the vast amount of data sets. One challenge in information retrieval is text classification. Current practices rely heavily on the classical naive Bayes algorithm due to its simplicity and robustness. However, results from this algorithm are not always satisfactory. In this article, the limitations of the naive Bayes algorithm are discussed, and it is found that the assumption on the independence of terms is the main reason for an unsatisfactory classification in many real-world applications. To overcome the limitations, the dependent factors are considered by integrating a term frequency–inverse document frequency TF-IDF weighting algorithm in the naive Bayes classification. Moreover, the TF-IDF algorithm itself is improved so that both frequencies and distribution information are taken into consideration. To illustrate the effectiveness of the proposed method, two simulation experiments were conducted, and the comparisons with other classification methods have shown that the proposed method has outperformed other existing algorithms in terms of precision and index recall rate.

Jing Qi

Papers

Feature weighted naïve Bayes algorithm for information retrieval of enterprise systems