Name
Title | ||
|---|---|---|
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming. |
Abstract | ||
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Graphical abstractDisplay Omitted HighlightsA heterogeneous flexible neural tree (FNT) for function approximation was proposed.FNT was studied under Pareto-based multiobjective genetic programming framework.A diversity-index was introduced to maintain diversity in genetic population.FNT was found competitive with other algorithm when cross validated over datasets.Evolutionary weighted ensemble of HFNTs further improved FNT performance. Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2016.09.035 | Appl. Soft Comput. |
Keywords | DocType | Volume |
Pareto-based multiobjectives,Flexible neural tree,Ensemble,Approximation,Feature selection | Journal | 52 |
Issue | ISSN | Citations |
C | Applied Soft Computing, 2017, Volume 52 Pages 909 to 924 | 6 |
PageRank | References | Authors |
0.42 | 59 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Varun Kumar Ojha | 1 | 32 | 9.25 |
| Ajith Abraham | 2 | 8954 | 729.23 |
| Václav Snasel | 3 | 1261 | 210.53 |