The nature of statistical learning theory
The nature of statistical learning theory
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Design and Analysis of Experiments
Design and Analysis of Experiments
A hybrid model for exchange rate prediction
Decision Support Systems
A New Density-Based Scheme for Clustering Based on Genetic Algorithm
Fundamenta Informaticae
Noise reduction and edge detection via kernel anisotropic diffusion
Pattern Recognition Letters
Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using domain-specific knowledge in generalization error bounds for support vector machine learning
Decision Support Systems
DECODE: a new method for discovering clusters of different densities in spatial data
Data Mining and Knowledge Discovery
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Density link-based methods for clustering web pages
Decision Support Systems
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
The data complexity index to construct an efficient cross-validation method
Decision Support Systems
A novel SVM+NDA model for classification with an application to face recognition
Pattern Recognition
Non parametric local density-based clustering for multimodal overlapping distributions
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
An approach to find embedded clusters using density based techniques
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
A non-linear quality improvement model using SVR for manufacturing TFT-LCDs
Journal of Intelligent Manufacturing
| Hi-index | 0.00 |
Small data set problems have been widely considered in many fields, where increasing the prediction ability is the most important goal. This study considers the data structure to identify new data points in a more precise manner, and is thus able to achieve improved prediction capability. The proposed method, named structure-based data transformation, consists of two steps. The first step is using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to separate data sets into clusters, which generates the number of clusters dynamically. The second step is to build up the data transformation function, in which the new attributes are computed using fuzzy membership functions obtained by the corresponding membership grades in each cluster. Three real cases are selected to compare the proposed forecasting model with the linear regression (LR), backpropagation neural network (BPNN), and support vector machine for regression (SVR) methods. The result show that the structure-based data transformation method has better performance than when using the raw data with regard to the error improving rate, mean square error (MSE), and standard deviation (STD).