Reach Us +44-1202-068036
Applying WEKA towards Machine Learning With Genetic Algorithm and Back-propagation Neural Networks | OMICS International
ISSN: 2153-0602
Journal of Data Mining in Genomics & Proteomics
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Applying WEKA towards Machine Learning With Genetic Algorithm and Back-propagation Neural Networks

Zeeshan Ahmed1,2* and Saman Zeeshan2

1Department of Neurobiology and Genetics, Biocenter, University of Wuerzburg, Germany

2Department of Bioinformatics, Biocenter, University of Wuerzburg, Germany

*Corresponding Author:
Zeeshan Ahmed
Department of Neurobiology and Genetics
University of Wuerzburg, Germany
Tel: +49-931-31-81917
Fax: +49-931-31-84452
E-mail: [email protected]

Received date: May 20, 2014; Accepted date: August 26, 2014; Published date: September 02,2014

Citation: Ahmed Z, Zeeshan S (2014) Applying WEKA towards Machine Learning With Genetic Algorithm and Back-propagation Neural Networks. J Data Mining Genomics Proteomics 5:157. doi:10.4172/2153-0602.1000157

Copyright: © 2014 Ahmed Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Data Mining in Genomics & Proteomics


Machine learning aims of facilitating complex system data analysis, optimization,classification and predictionwith the use of different mathematical and statistical algorithms. In this research, we are interested in establishing the process of estimating best optimal input parameters to train networks. Using WEKA, this paper implements a classifier with Back-propagation Neural Networks and Genetic Algorithm towards efficient data classification and optimization.The implemented classifier is capable of reading and analyzing a number of populations in giving datasets, and based on the identified population it estimates kinds of species in a population, hidden layers, momentum, accuracy, correct and incorrect instances.


Back Propagation Neural Network; Genetic Algorithm; Machine learning; WEKA


Machine learning [1] is a branch of Artificial Intelligence, facilitating probabilistic system development for complex data analysis, optimization, classification and prediction. Different learning methods have been introduced e.g. supervised learning, unsupervised learning, semi supervised learning, reinforcement learning, transduction learning and learning to learn etc.

Several statistical algorithms (e.g. Genetic Algorithm [2], Bayesian statistics [3], Case-based reasoning [4], Decision trees [5], Inductive logic programming [6], Gaussian process regression [7], Group method of data handling [8], k-NN [9], SVMs [10], Ripper [11], C4.5 [12] and Rule-based classifier [13] etc.) have been proposed for the learning behavior implementation. The criterion for choosing a mathematical algorithm is based on the ability to deal with the weighting of networks, chromosome encoding and terminals.

Different machine learning approaches have been proposed towards the implementation of adaptive machine learning systems and data classification e.g. Fast Perceptron Decision Tree Learning [14], Massive Online Analysis (MOA) [15], 3D Face Recognition Using Multi view Key point Matching [16], Evolving Data Streams [17], Classifier Chain [18], Multi-label Classification [19], Multiple-Instance Learning [20], Adaptive Regression [21], nearest neighbor search [22], Bayesian network classification [23,24], Naive Bayes text classification [25], ML for Information Retrieval [26], Probabilistic unification grammars [27], Instance Weighting [28], KEA [29] and Meta Data for ML [30] etc. Apart from the fact of existence of these referred valuable approaches, we have decided to implement our own software application during this research and development, consisting of different methodology.

In this research, we are interested in finding the most suitable algorithm to establish the process of estimating best optimal input parameters and on the basis the selected parameters, train network to best fit with the use of suitable learning techniques. We discuss a script implementing the Genetic Algorithm for data optimization and back propagation neural network algorithm for the learning behavior. The objective is to analysis different datasets based on the number of attributes, classes, instances and relationships. Following the agenda (Section 1), this short paper is organized in the upcoming sections: data classifier and its methodology explain in section 2, validation is performed in section 3 and observed results are concluded in section 4.

Optimal Data Classifier

The implemented classifier is proficient in reading and analyzing a number of populations in giving datasets. The classifier is developed using Java programming language and WEKA library [31,32]. The WEKA library provides built-in classes and functions to implement and utilize different mathematical and statistical algorithms (e.g. genetic algorithm and back propagation algorithm etc.) for data classification.

Based on the number of identified population, it estimates following results: kinds of species in a population (if there are more than 1), correctly classified instances, incorrectly classified instances, hidden layers, momentum and accuracy (optimized, weighted results).

The classifier is capable of processing standard Attribute Relation File Format (ARFF) dataset files, which describes the list of instances sharing a set of attributes, especially used to develop for machine learning projects. The classifier’s workflow starts with the analysis of inputted data and extraction of attributes, classes, instances and relationships. In the next step classifier extracts the information about number of hidden layers, learning rate and momentum to identify correctly and incorrectly classified instances. At the final step, classify the data using Back Propagated Neural Network for Multilayer Perception and optimize results using Genetic Algorithm.

During data classification using the genetic algorithm; first chromosomes are estimated, then learning rate and momentum is set to perform cross over using a pair of the best results (Figure 1). The next the mutation of two offspring is performed on the basis of obtained accuracies of two previously estimated offspring. The offspring with lower values are replaced with two new offspring. In the last steps, after cross validation, the individual and commutative weights of instances are calculated. The obtained results are validated and final output is presented to the user in the end. The measurement and prediction procedure can be repeated until the satisfactory results are achieved.


Figure 1: Data Classification -The Figure 1 presents the application of Genetic Algorithm for data classification. The method estimates chromosomes, sets learning rate and momentum based calculated chromosomes, crosses over using pair of best chromosomes, mutates new off springs, replaces offspring, perform cross validation, calculates individual and commutative weights of all instances.


We have validated the classifier using two different data sets: Zoo database ( and Labor database ( Zoo database contains 101 Instances with of 18 Attributes; 2 numeric attributes (animal and legs) and 16 Booleans attributes (hair, feather, eggs, milk, airborne, aquatic, predator, toothed, backbone, breathes, venomous, fins, legs, tail, domestic, cat size and type). Whereas the Labor database comprises of 57 Instances including of 16 Attributes; 13 numeric attributes (duration, wage increase first year, wage increase second year, wage increase third year, cost of living adjustments, working hours, pension, standby pay, shift differential, statutory holidays, vacations, contribution dental plan and contribute to health plan) and 3 Boolean attributes (bereavement assistance, long term disability assistance and education alliance) (Table 1).

Zoo Database Labor Database
1617 Mammals, 539 Birds, 0 Reptile, 637 Fish, 0 Amphibian, 490 Insects and 49 Invertible from the whole population of 3332 species in dataset. 49 Good and 441 Bad of all 490 Population.
68 instances are correctly classified and rest 33 are incorrectly classified from all 101 instances 10 instances are correctly classified and rest 47 are incorrectly classified from all 57 instances
No Hidden layer No Hidden layer
0.3 Learning rate 0.1 Learning rate
0.1 Momentum 0.5 Momentum
0.67326732673267326733 Accuracy 0.17543859649122806 Accuracy

Table 1: Results of Data classification.

Both datasets are analyzed using implemented classifier, using WEKA explorer (Figure 2A and 2C). The observed results are (Figure 2B and 2D) are presented in Table 1. We have validated the classifier in three ways: (1) by increasing the learning rate and placing the momentum constant, (2) by increasing both learning rate and momentum and (3) by randomly changing the weight. During the validation process the size of the chromosome was 6 bits, 3 bit decimal value (0-10/10=value) for learning rate and 3 bit decimal values for momentum.


Figure 2: WEKA Graphical User Interface:The Figure 2(A) presents the example data set Zoo Database being processed using WEKA Explorer and (2B) presents the obtained results. Whereas the Figure 2(C) presents the example data set Labor Database being processed using WEKA Explorer and (2C) presents the obtained results.


We have observed during the validation process that by keeping the default weight of instance, the results become stable but by increasing the weight of instance the size of results increases. The findings lead to the outcome that mutation can affect the accuracy by increasing and decreasing it. Moreover, we have also observed that classifier produces results in minimum possible time with value 1, and if we will increase the value of classifier it will take more time.

Conflict of Interest

The author declares no conflict of interest.


We would like to thank Deutsche Forschungsgemeinschaft (DFG SFB 1047/Z) for funding and University of Wuerzburg Germany for the support.We thank anonymous reviewers for helpful comments on the manuscript and the Land of Bavaria Germany.


Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Article Usage

  • Total views: 14706
  • [From(publication date):
    August-2014 - Dec 12, 2018]
  • Breakdown by view type
  • HTML page views : 10676
  • PDF downloads : 4030

Review summary

  1. Nkika
    Posted on Sep 21 2016 at 12:58 pm
    your work is very interested .Let me ask you the weka version that you use to train Neural network using genetic algorithm

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2018-19
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri and Aquaculture Journals

Dr. Krish

[email protected]

+1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals


[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry


[email protected]

1-702-714-7001Extn: 9042

© 2008- 2018 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
Leave Your Message 24x7