Feature is very difficult to identify the exact pattern.

Feature Selection

The important objective of feature selection is to find the minimal feature from the original data and to obtain high accuracy result. If we have a large number of data’s it is very difficult to identify the exact pattern. This feature selection technique is used to remove the redundant, misleading, noisy, irrelevant signal from the pre-processed data. This process helps to select the best feature and presented to the classifier for accurate classification which minimizes the cost of the system and this helps to identify the accuracy. In this work, modified techniques are given and compared with the previous work and the standard algorithm and show that the modified technique gives the best result for the arrhythmia disease classification. Generally, noise reduction is the main issue which is to be solved by extracting the preferred noiseless signal for processing the further work. One of the most challenging tasks is feature selection because it searches in large space and the size of the space increases based on the number of data set present in the data set. In the proposed work of feature selection method the modified PSO algorithm, Modified BFO, and BAT algorithm are compared and presented in the preceding section.

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Standard PSO Algorithm      

       PSO algorithm is an adequate strategy that finds or searches the data in global search ability. In PSO is Particle Swarm Optimization algorithm where each solution can be symbolized as a particle in the swarm. Every particle has a position in search space, it can be represented by a vector Xi= {Xi1, Xi2, Xi3, ….XiD},where D represents the dimensionality of the search space. It finds the optimal solution in the search space. Each and every particle has velocity, and it can be represented as Vi {Vi1.Vi2, vi3 ,….ViD}.The position and the velocity are updated for each particle when it moves from one space to another space. The best position is determined and the prior position is named as Pbest and the best position is called as Gbest, predicted on the Pbest and Gbest the particle is updated to find the optimal solution.The new position can be identified by the given equation:

Cons of Standard PSO Algorithm

·         Standard PSO algorithm is best suited for one particular search space and not search in global.

·         During the iterative process it has a low convergence rate.

·         If the particle dimension size is increased then the storage space is also growing and the time computation is also high.

·         Due to these issues the efficiency is lower in high dimensional space.