Quadratically constrained quadratic programming for classification using particle swarms and applications
This provides an incremental alternative method for classification problems, potentially benefiting researchers in optimization and machine learning.
The authors tackled binary classification by formulating it as a quadratically constrained quadratic programming problem, solved using particle swarm optimization, and achieved performance close to SVM and better than a neural network on datasets like Iris and Pima.
Particle swarm optimization is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The approach of particle swarms is an example for interior point methods in optimization as an iterative technique. This approach is novel and deals with classification problems without the use of a traditional classifier. Our method determines the optimal hyperplane or classification boundary for a data set. In a binary classification problem, we constrain each class as a cluster, which is enclosed by an ellipsoid. The estimation of the optimal hyperplane between the two clusters is posed as a quadratically constrained quadratic problem. The optimization problem is solved in distributed format using modified particle swarms. Our method has the advantage of using the direction towards optimal solution rather than searching the entire feasible region. Our results on the Iris, Pima, Wine, and Thyroid datasets show that the proposed method works better than a neural network and the performance is close to that of SVM.