DeepAI AI Chat
Log In Sign Up

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

by   Varun Kumar Ojha, et al.

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.


page 1

page 2

page 3

page 4


Modified Soft Brood Crossover in Genetic Programming

Premature convergence is one of the important issues while using Genetic...

Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression

We introduce in this paper a runtime-efficient tree hashing algorithm fo...

Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

In this work, a computational intelligence (CI) technique named flexible...

Evolutionary bagging for ensemble learning

Ensemble learning has gained success in machine learning with major adva...

Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees

This paper proposes a design of hierarchical fuzzy inference tree (HFIT)...

Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

Diversity represents an important aspect of genetic programming, being d...

Evolutionary Ensemble Learning for Multivariate Time Series Prediction

Multivariate time series (MTS) prediction plays a key role in many field...