  This paper proposes a new generalized two dimensional learning approach for
particle swarm based feature selection. The core idea of the proposed approach
is to include the information about the subset cardinality into the learning
framework by extending the dimension of the velocity. The 2D-learning framework
retains all the key features of the original PSO, despite the extra learning
dimension. Most of the popular variants of PSO can easily be adapted into this
2D learning framework for feature selection problems. The efficacy of the
proposed learning approach has been evaluated considering several benchmark
data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The
results of the comparative investigation including the time-complexity analysis
with GA, ACO and five other PSO variants illustrate that the proposed 2D
learning approach gives feature subset with relatively smaller cardinality and
better classification performance with shorter run times.
