شماره ركورد كنفرانس :
4418
عنوان مقاله :
EM-ACO: Combining EM and ACO for Classification
پديدآورندگان :
Zhiani Asoudeh Toktam Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran , Javadi Hamid Haj Seyyed Department of Mathematics and Computer Science, Shahed University, Tehran, Iran , saboori Neda Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
كليدواژه :
Classification , Hybrid method , Ant colony optimization algorithm , Expectation maximization algorithm
عنوان كنفرانس :
يازدهمين كنفرانس سراسري سيستم هاي هوشمند
چكيده فارسي :
Classification is a data mining technique that assigns data in a collection to target classes. Ant Colony Optimization (ACO) and Expectation Maximization (EM) algorithms are effective methods of machine learning which have been used widely for classification. In this work, a hybrid method is proposed by using these algorithms, as named EM-ACO. In the approach, ACO is first used to improve training data, and then EM classifies improved dada. We test our method on UCI machine learning data sets, iris and glass. The experimental results show proposed method performs better than EM
