Class dependent feature scaling method using naive Bayes classifier for text datamining
ABSTRACT The problem of feature selection is to find a subset of features for optimal classification. A critical part of feature selection is to rank features according to their importance for classification. The naive Bayes classifier has been extensively used in text categorization. We have developed a new feature scaling method, called class–dependent–feature–weighting (CDFW) using naive Bayes (NB) classifier. A new feature scaling method, CDFW–NB–RFE, combines CDFW and recursive feature elimination (RFE). Our experimental results showed that CDFW–NB–RFE outperformed other popular feature ranking schemes used on text datasets.
Conference Proceeding: Human-humanoid robot interaction system based on spoken dialogue and vision[show abstract] [hide abstract]
ABSTRACT: In this paper, a natural human-humanoid robot interaction system is proposed. In this system, the type of task is determined with naïve Bayes classifier firstly, and then most of the conditions which are prerequisite to the execution of task are calculated with frame-based reasoning method. The initial position of task object must be calculated with vision. First, the hand position is localized with temporal differencing algorithm. Then, the object region can be determined with the pointing direction of hand. Finally, the reference position of task object in world coordinate system is calculated with binocular vision system.Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on; 08/2010