Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Classification and regression by combining models
Classification and regression by combining models
Machine-Learning Techniques for Software Product Quality Assessment
QSIC '04 Proceedings of the Quality Software, Fourth International Conference
Predicting carcinoid heart disease with the noisy-threshold classifier
Artificial Intelligence in Medicine
Classifier ensembles: Select real-world applications
Information Fusion
Feature selection for text classification with Naïve Bayes
Expert Systems with Applications: An International Journal
A novel composite model approach to improve software quality prediction
Information and Software Technology
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
IEEE Computational Intelligence Magazine
| Hi-index | 0.00 |
The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.