Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
MATLAB code for BMOGWO-S: Binary Multi-objective Grey Wolf Optimizer for Feature selection in Classification Paper Reference - Q. Al-Tashi et al., “Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification,” IEEE Access, vol. 8, pp. 106247–106263, 2020. Link for algorithm details: Paper https://ieeexplore.ieee.org/abstract/document/9108264
To Run the code follow the following steps:
BMOGWO-S:
- To execute the algorithm run BMOGWOS.m file
- To change the dataset go to LoadData.m file
- To find the error rate of all features of the data Run ErroRate.m
- We run BMOGWOS.mat 20 times for each dataset and then we choose the best solution
- You have to create a folder for each data to stored the non-dominated solution.
- we averaged the computational time obtained in 20 runs.
- Noted that the time obtained is in seconds you have to convert it to minutes.
- We have used only ANN classifier to obtain error rate,you can use other classifiers as well. But, you need to change the CreateAndTrainANN.m file.
Abstract:
Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously.
Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to
solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection
problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGWO-S is developed to optimize feature selection problems.
A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method,
15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II)
and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions.
The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.
