Authors:
Saleh Alyahyan
;
Majed Farrash
and
Wenjia Wang
Affiliation:
University of East Anglia, United Kingdom
Keyword(s):
Heterogeneous Ensemble, Diversity, Big Data, Scene Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Clustering and Classification Methods
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Foundations of Knowledge Discovery in Databases
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Multimedia Data
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
Abstract:
In data mining, identifying the best individual technique to achieve very reliable and accurate classification has always been considered as an important but non-trivial task. This paper presents a novel approach - heterogeneous ensemble technique, to avoid the task and also to increase the accuracy of classification. It combines the models that are generated by using methodologically different learning algorithms and selected with different rules of utilizing both accuracy of individual modules and also diversity among the models. The key strategy is to select the most accurate model among all the generated models as the core model, and then select a number of models that are more diverse from the most accurate model to build the heterogeneous ensemble. The framework of the proposed approach has been implemented and tested on a real-world data to classify imaginary scenes. The results show our approach outperforms other the state of the art methods, including Bayesian network, SVM a
nd AdaBoost.
(More)