Feature Selection using Harmony Search Optimization Algorithm for Handwritten Script Identification

What is it about?

The feature selection process can be considered as a problem of global combinatorial optimization in machine learning, which reduces the irrelevant, noisy and non-contributing features resulting in acceptable classification accuracy. Harmony Search Algorithm (HSA) is an evolutionary algorithm which is applied to various optimization problems such as scheduling, text summarization, water distribution networks and vehicle routing, etc. This paper presents a hybrid approach based on Support Vector Machine (SVM) classifier and Harmony Search optimization algorithm for wrapper feature subset selection.

Featured Image

Why is it important?

The assessment justifies the need of feature selection for handwritten script identification where local and global feature are computed without knowing the exact importance of features. The proposed approach is compared with four well-known evolutionary algorithms namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), Ant Colony Optimization (ACO) and two statistical feature dimensionality reduction techniques namely, Greedy Attribute Search (GAS) and Principal Component Analysis (PCA). The acquired results show that the optimal set of features selected using HSA gives better accuracy in handwritten script recognition.

Perspectives