Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Distance-based features in pattern classification | Tsai et al. EURASIP Journal on Advances in Signal Processing 2011 2011 62 http content 2011 1 62 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Distance-based features in pattern classification Chih-Fong Tsai1 Wei-Yang Lin2 Zhen-Fu Hong1 and Chung-Yang Hsieh2 Abstract In data mining and pattern classification feature extraction and representation methods are a very important step since the extracted features have a direct and significant impact on the classification accuracy. In literature numbers of novel feature extraction and representation methods have been proposed. However many of them only focus on specific domain problems. In this article we introduce a novel distance-based feature extraction method for various pattern classification problems. Specifically two distances are extracted which are based on 1 the distance between the data and its intra-cluster center and 2 the distance between the data and its extra-cluster centers. Experiments based on ten datasets containing different numbers of classes samples and dimensions are examined. The experimental results using naive Bayes k-NN and SVM classifiers show that concatenating the original features provided by the datasets to the distance-based features can improve classification accuracy except image-related datasets. In particular the distance-based features are suitable for the datasets which have smaller numbers of classes numbers of samples and the lower dimensionality of features. Moreover two datasets which have similar characteristics are further used to validate this finding. The result is consistent with the first experiment result that adding the distance-based features can improve the classification performance. Keywords distance-based features feature extraction feature representation data mining cluster center pattern classification 1. Introduction Data mining has received unprecedented focus in the recent years. It can be
