Efficient Instance Selection Based on Spatial Abstraction

Machine learning approaches have been applied in huge volumes of data. In order to deal with this big data, techniques for instance selection have been applied for reducing the data to a manageable volume and, consequently, for reducing the computational resources that are necessary to apply machine learning approaches. In this paper, we propose an efficient approach for instance selection called ISDSP. It adopts the notion of spatial partition for efficiently splitting the dataset in sets of similar instances. In a second step, the algorithm selects a representative instance of each of the densest spatial partitions that were previously identified. The approach was evaluated on 15 well-known datasets used in a classification task, and its performance was compared to those of 6 state-of-the-art algorithms, considering two measures: accuracy and reduction. All the obtained results show that, in general, the proposed approach provides a good trade-off between accuracy and reduction, with a significantly lower running time, when compared with other approaches.

[1]  Chris Mellish,et al.  Advances in Instance Selection for Instance-Based Learning Algorithms , 2002, Data Mining and Knowledge Discovery.

[2]  Mara Abel,et al.  An Efficient Prototype Selection Algorithm Based on Spatial Abstraction , 2018, DaWaK.

[3]  Khalid M. Salama,et al.  Instance Selection with Ant Colony Optimization , 2015, INNS Conference on Big Data.

[4]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[5]  Francisco Herrera,et al.  Data Preprocessing in Data Mining , 2014, Intelligent Systems Reference Library.

[6]  Tony R. Martinez,et al.  Reduction Techniques for Instance-Based Learning Algorithms , 2000, Machine Learning.

[7]  Mara Abel,et al.  An Efficient Prototype Selection Algorithm Based on Dense Spatial Partitions , 2018, ICAISC.

[8]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[9]  Q. Henry Wu,et al.  A class boundary preserving algorithm for data condensation , 2011, Pattern Recognit..

[10]  Joel Luis Carbonera,et al.  An Efficient Approach for Instance Selection , 2017, DaWaK.

[11]  Hadi Sadoghi Yazdi,et al.  IRAHC: Instance Reduction Algorithm using Hyperrectangle Clustering , 2015, Pattern Recognit..

[12]  Chien-Hsing Chou,et al.  The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[13]  Joel Luis Carbonera,et al.  A Novel Density-Based Approach for Instance Selection , 2016, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).

[14]  G. Gates.  The Reduced Nearest Neighbor Rule , 1998 .

[15]  William Eberle,et al.  Learning to detect representative data for large scale instance selection , 2015, J. Syst. Softw..

[16]  Joel Luis Carbonera,et al.  Efficient Prototype Selection Supported by Subspace Partitions , 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).

[17]  Antonio González Muñoz,et al.  Three new instance selection methods based on local sets: A comparative study with several approaches from a bi-objective perspective , 2015, Pattern Recognit..

[18]  Wei Fan,et al.  Mining big data: current status, and forecast to the future , 2013, SKDD.

[19]  Joel Luis Carbonera,et al.  A Density-Based Approach for Instance Selection , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).