RT Conference Proceedings
T1 Analysis and Application of Normalization Methods with Supervised Feature Weighting to Improve K-means Accuracy
A1 Niño-Adan, Iratxe
A1 Landa-Torres, Itziar
A1 Portillo, Eva
A1 Manjarres, Diana
A2 Martínez Álvarez, Francisco
A2 Troncoso Lora, Alicia
A2 Quintián, Héctor
A2 Sáez Muñoz, José António
A2 Corchado, Emilio
AB Normalization methods are widely employed for transforming the variables or features of a given dataset. In this paper three classical feature normalization methods, Standardization (St), Min-Max (MM) and Median Absolute Deviation (MAD), are studied in different synthetic datasets from UCI repository. An exhaustive analysis of the transformed features’ ranges and their influence on the Euclidean distance is performed, concluding that knowledge about the group structure gathered by each feature is needed to select the best normalization method for a given dataset. In order to effectively collect the features’ importance and adjust their contribution, this paper proposes a two-stage methodology for normalization and supervised feature weighting based on a Pearson correlation coefficient and on a Random Forest Feature Importance estimation method. Simulations on five different datasets reveal that our two-stage proposed methodology, in terms of accuracy, outperforms or at least maintains the K-means performance obtained if only normalization is applied.
PB Springer Verlag
SN 9783030200541
SN 2194-5357
YR 2020
FD 2020
LK https://hdl.handle.net/11556/2102
UL https://hdl.handle.net/11556/2102
LA eng
NO Niño-Adan , I , Landa-Torres , I , Portillo , E & Manjarres , D 2020 , Analysis and Application of Normalization Methods with Supervised Feature Weighting to Improve K-means Accuracy . in F Martínez Álvarez , A Troncoso Lora , H Quintián , J A Sáez Muñoz & E Corchado (eds) , 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings . Advances in Intelligent Systems and Computing , vol. 950 , Springer Verlag , pp. 14-24 , 14th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2019 , Seville , Spain , 13/05/19 . https://doi.org/10.1007/978-3-030-20055-8_2
NO conference
NO Publisher Copyright: © 2020, Springer Nature Switzerland AG.
NO Acknowledgement. This work has been supported in part by the ELKARTEK program (SeNDANEU KK-2018/00032), the HAZITEK program (DATALYSE ZL-2018/00765) of the Basque Government and a TECNALIA Research and Innovation PhD Scholarship.
DS TECNALIA Publications
RD 28 jul 2024