Evolutionary algorithms have been successfully employed to solve classification problems. However, what I believe that hybridization of evolutionary approach have better precision rate as compared to their individual use. Moreover, other factors like nature of data, features selection, pre and post processing should also be considered

Dear Sir,

Recently, there have been works that addressed this problem

by proposing models of performance of different methods such as Genetic Programming Systems.

U can read this article

Conference Paper Performance Classification of Genetic Algorithms on Continuo...

I think neural fuzzy classifier still have upper hand for solving these problems. off course how one can forget the Support Vector Machines

Most evolutionary algorithms are good so far for classification problem. In general, it depends on researcher view point and justification as well as the datasets that work on it

Evolutionary algorithms have been recently used as a solution to some issues when dealing with datasets

for example : optimising the SVM kernel paramters , undersampling/oversampling and many others...

Most evolutionary algorithms can be used to solve the classification problem, which like use evolutionary algorithms to solve clustering problem. If the extended model be used, most classification accuracies are 100% on datasets. Let me know if you have interests.

As we know classification problems are of two types:

1. Supervised Learning (Classes are known)

2. Unsupervised Learning (Classes are unknown)

Now the question before us is:

1. How to use EAs in classification problem?

Now let's discuss the answer briefly

1. How to use EAs in classification problem?

A. Supervised Learning: In supervised learning EA is used either to tune some parameters of classification model (Support Vector Machine, Naive Bayes, K-Nearest Neighbour, etc.) or to select optimal number of features to achieve desire level of accuracy in less amount of time.

(i) The main purpose of tuning the classification model is to optimize the decision boundary for better learning and classification.

(ii) The major problem with the classification model is over fitting or curse of dimensionality. This problem arises due to the presence of redundant or repeatitive features. Therefore, EA helps in finding the optimal set of features which can improve the classification accuracy as well as significantly reduces the learning and classification time.

(iii) During experiment we also observed that these approaches can greatly impact the accuracy.

B. Unsuprevised Learning: Unsupervised learning is more of clustering approach in which the data are clustered on the

basis of similarity. Kmeans, Fuzzy C-means etc. are the most common and popular clustering approaches. Therefore, here

EA can be used to cluster the data according to the user defined objective.

Latest research published in PLOS and Nature on the same problem

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173907

http://www.nature.com/articles/s41598-017-00416-0

Hope it will help!

Thank you all for your answers

Conference Paper A classification method based on self-adaptive artificial bee colony

Evolutionary algorithms have been successfully employed to solve classification problems. However, what I believe that hybridization of evolutionary approach have better precision rate as compared to their individual use. Moreover, other factors like nature of data, features selection, pre and post processing should also be considered