TY - GEN
T1 - Joint feature transformation and selection based on Dempster-Shafer theory
AU - Lian, Chunfeng
AU - Ruan, Su
AU - Denoeux, Thierry
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - In statistical pattern recognition, feature transformation attempts to change original feature space to a low-dimensional subspace, in which new created features are discriminative and non-redundant, thus improving the predictive power and generalization ability of subsequent classification models. Traditional transformation methods are not designed specifically for tackling data containing unreliable and noisy input features. To deal with these inputs, a new approach based on Dempster-Shafer Theory is proposed in this paper. A specific loss function is constructed to learn the transformation matrix, in which a sparsity term is included to realize joint feature selection during transformation, so as to limit the influence of unreliable input features on the output low-dimensional subspace. The proposed method has been evaluated by several synthetic and real datasets, showing good performance.
AB - In statistical pattern recognition, feature transformation attempts to change original feature space to a low-dimensional subspace, in which new created features are discriminative and non-redundant, thus improving the predictive power and generalization ability of subsequent classification models. Traditional transformation methods are not designed specifically for tackling data containing unreliable and noisy input features. To deal with these inputs, a new approach based on Dempster-Shafer Theory is proposed in this paper. A specific loss function is constructed to learn the transformation matrix, in which a sparsity term is included to realize joint feature selection during transformation, so as to limit the influence of unreliable input features on the output low-dimensional subspace. The proposed method has been evaluated by several synthetic and real datasets, showing good performance.
KW - Belief functions
KW - Dempster-Shafer theory
KW - Feature selection
KW - Feature transformation
KW - Pattern classification
UR - https://www.scopus.com/pages/publications/84977126537
U2 - 10.1007/978-3-319-40596-4_22
DO - 10.1007/978-3-319-40596-4_22
M3 - 会议稿件
AN - SCOPUS:84977126537
SN - 9783319405957
T3 - Communications in Computer and Information Science
SP - 253
EP - 261
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 16th International Conference, IPMU 2016, Proceedings
A2 - Vieira, Susana
A2 - Kaymak, Uzay
A2 - Carvalho, Joao Paulo
A2 - Lesot, Marie-Jeanne
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer Verlag
T2 - 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016
Y2 - 20 June 2016 through 24 June 2016
ER -