PREDICTING PERFORMANCE OF CLASSIFICATION ALGORITHMS

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
ISSN 0976 - 6375(Online), Volume 6, Issue 2, February (2015), pp. 19-28 © IAEME
19
PREDICTING PERFORMANCE OF CLASSIFICATION
ALGORITHMS
Firas Mohammed Ali1
, Dr. Prof. El-Bahlul Emhemed Fgee2
, Dr.Prof.Zakaria Suliman Zubi3
1
B.Sc IT Student, IT Department, Libyan Academy, Tripoli, Libya,
2
Supervisor, Computer Department, Libyan Academy, Tripoli, Libya,
3
External Guide, Sirt University, Sirt, Libya,
ABSTRACT
Classification is the most commonly applied data mining method, and is used to develop
models that can classify large amounts of data to predict the best performance. Identifying the best
classification algorithm among all available is a challenging task. This paper presents a performance
comparative study of the most widely used classification algorithms. Moreover, the performances of
these algorithms have been analyzed by using different data sets. Three different datasets from
University of California, Irvine (UCI) are compared with different classification techniques. Each
technique has been evaluated with respect to accuracy and execution time and performance
evaluation has been carried out with selected classification algorithms. The WEKA machine learning
tool is used to analysis of these three different data sets based on applying these classification
methods to selected datasets and predicting the best performance results.
Keywords: Classification Algorithms, Weka, LMT, Random Tree, Neive Base
I. INTRODUCTION
Nowadays there is huge amount of data being collected and stored in databases everywhere
across the globe. The tendency is to keep increasing year after year. It is not hard to find databases
with Terabytes of data in enterprises and research facilities. That is over 1012 bytes of data. There is
invaluable information and knowledge “hidden” in such databases; and without automatic methods
for extracting this information it is practically impossible to mine for them [1]. Throughout the years
many algorithms were created to extract what is called nuggets of knowledge from large sets of data.
There are several different methodologies to approach this problem: classification. Classification is a
data mining (machine learning) technique used to predict group membership for data instances. For
example, you may wish to use classification to predict whether the weather on a particular day will
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 6, Issue 2, February (2015), pp. 19-28
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be sunny, rainy or cloudy. Popular classification techniques include decision trees and neural
networks. It involves using a training set of data that contains observations to identify which
categories each observation should be placed in. Individual observations are analyzed and grouped in
explanatory variables, which may have categorical, ordinal, integer-valued, or real-valued properties.
Figure.1 shows the classification process.
II. PROBLEM DESCRIPTION
Classification consists of predicting a certain outcome based on a given input. In order to
predict the outcome, the algorithm processes a training set containing a set of attributes and the
respective outcome, usually called goal or prediction attribute. The algorithm tries to discover
relationships between the attributes that would make it possible to predict the outcome. Next the
algorithm is given a data set not seen before, called prediction set, which contains the same set of
attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and
produces a prediction. The prediction accuracy defines how “good” the algorithm is. For example, in
a medical database the training set would have relevant patient information recorded previously,
where the prediction attribute is whether or not the patient had a heart problem [2].
III. THE SELECTED CLASSIFICATION ALGORITHMS USED IN WEKA
These are the selected WEKA algorithms I chose to analyze since whey where implemented
in the WEKA suite and ready to use directly. The decision to use the following algorithms was based
on the efficiencies seen in the reports I read about data classification. I tried to pick at least one type
of classifier from each of the major classifier groups and ended up with the following below is a
small description of as follows:
a) Naive Bayes
This was a very simple classifier that performed decent and should be easy to implement
regardless of language used. The drawback was that it wasn't in the top when it came to classifying
instances correctly. This was however not a big drawback since it was quick at constructing a
classification model, as well as classifying data [3].
b) SMO
Sequential minimal optimization algorithm that uses support vectors. Has built in support to
handle multiple classes using pairwise classification. Note that this algorithm is of the lazy type
which does all the calculations [3].
c) KStar (K*)
Aha, Kibler & Albert describe three instance-based learners of increasing sophistication. IB1
is an implementation of a nearest neighbor algorithm with a specific distance function. IB3 is a
further extension to improve tolerance to noisy data. Instances that have a sufficiently bad
classification history are forgotten and only instances that have a good classification history are used
for classification [4].
d) AdaBoostM1
Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal
class problems can be tackled. Often dramatically improves performance, but sometimes over fits
[4].

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e) JRip
A decent classifier that performed okay even though I had higher expectations of this rule
learner due to the reports I saw where it had been used. The drawback of this classifier was that it
requires an extremely long time to construct a classification model for big data sets when using a
high WTK value to a point where it becomes useless. For example, it required almost 54 hours to
construct a classification model for data set B in chapter 4.2 meanwhile the Naive Bayes classifier
managed to do the same in under 3 minutes [3]..
f) OneR
Class for building and using a 1R classifier; in other words, uses the minimum-error attribute
for prediction, discretizing numeric attributes [4].
g) PART
Class for generating a PART decision list. PART uses the separate-and-conquer strategy,
where it builds a rule in that manner and removes the instances it covers, and continues creating rules
recursively for the remaining instances. Where C4.5 and RIPPER does global optimization to
produce accurate rule sets, this added simplicity is the main advantage of PART [4].
h) J48
An open source implementation of the C4.5 algorithm that builds a decision tree using
information entropy. That means that when building the decision tree, C4.5 will at each node select
the attribute that most successfully splits its set of samples seen to the difference in entropy that the
selected subtree generates [3].
i) LMT
Classifier for building 'logistic model trees', which are classification trees with logistic
regression functions at the leaves. The algorithm can deal with binary and multi-class target
variables, numeric and nominal attributes and missing values [4].
j) Random tree
Class for constructing a tree that considers K randomly chosen attributes at each node.
Performs no pruning [4]
IV. DEVELOPMENT
How a method to analyze data can be constructed are discussed and implemented. And also
discuss how different algorithms perform when classifying data. In theory, using a big data set to
construct the classifier model will increase the performance when classifying new data since it would
be easier to construct a more general model and hence finding a suitable match for our dataset. The
optimal size of the data set used to construct the classifier model is dependent on a number of things
such as the size of the classification problem, the classifier algorithm used and the quality of the data
set. The goal was to see how well the different algorithms performed, not just by comparing the
number of correct classifications, but also by looking into the time required to construct the
classification model depending on the size of the input data and number features used of as well as
the time required to classify a data set using the generated classification model. It was entirely
possible to implement these algorithms into classifiers from scratch since there were a lot of
documentations describing them. Mainly three data sets used in this thesis are again taken from the
UCI data sets [5, 6].

22
V. CLASSIFICATION USING WEKA- IMPLEMENTATION STEPS
Step 1. Open WEKA Application
Start > All Programs > WEKA 3.7.11 > WEKA 3.7
Step 2. Load a Dataset file
Explorer > Open file… > Local Disk (C :) > Program Files > Weka-3-7 > data >
“ select dataset file”
Figure 1: Load a Dataset file
Step 3. Building “Classifiers”
Classify > Choose > “ select the classifier name “
Figure 2: Building “Classifiers”

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Step 4.Load the Test Option
Click on ‘Choose’ button in the ‘Classifier’ box just below the tabs and select C4.5 classifier WEKA
-> Classifiers -> Trees ->J48.
Figure 3: Load the Test Option
VI. DATA SET INFORMATION
Three data sets used in this for predicting performance with selected classification
algorithms.
Table 1: Credit German dataset information
Dataset Instances Attributes Data Type
Credit-g 1000 21 String
Table 2: Ionosphere dataset information
Ionosphere 351 35 Numeric
Table 3: Vote dataset information
Vote 435 17 Nominal
VII. RESULTS AND DISCUSSIONS
In this paper to evaluate performance of selected tool using the given datasets, several
experiments are conducted. For evaluation purpose, three test modes are used, the training set, the
cross-validation mode and percentage split mode. At the end, the recorded measures are averaged. It
is common to have 66% of the objects of the original database as a training set and the rest of objects
as a test set.There's a few more variables to considered before making the final decision, but from the
performance seen in earlier chapters, the proposed solution for how researchers should tackle the
problem of classifying structured data in there data sets is to implement a solution. The reason why

24
Random Tree is proposed instead of the other two candidates AdaBoostM1 and LMT that also
managed to reach the goal of a positive classification 100% percentage three times , whereas LMT
classification percentage perform 75.90 % and 77.06 %.Some predictive performance accuracies
given as an example in Table 4,5 and Table 6 shows best accuracy results highlighted in red and blue
colors with respect to the percentage split test mode, cross fold and training set on the three selected
UCI data sets such as German credit data, ionosphere and vote data sets [7].
Table 4: Comparison of classifiers using German Credit Data set in Percentage split mode

25
Table 5: Comparison of classifiers using ionosphere Data set in Cross-validation mode

26
Table 6: Comparison of classifiers using vote Data set in Training set mode
Table 7: Predictive performance of credit.g dataset
TestMode High accuracy
TrainingSet RandomTree
Crossfolds 10 LMT
Percentage split LMT
Table 8: Predictive performance of ionosphere dataset
Crossfolds 10 LMT
Percentage split AdaBoostM1

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Table 9: Predictive performance of Vote dataset
Crossfolds 10 J48
Percentage split AdaBoostM1
Figure 4: Tree analysis of Highest Performance Algorithms
VIII. CONCLUSION
Classification is one of the data mining tasks that applied in many area especially in medical
applications. One reason for using this technique is selecting the appropriate algorithm for each data
type .There is no algorithm that is the best for all classification domains. This paper results is a way
to select the proper algorithm for a particular domain with respect the test modes. Due to this, in my
opinion the RandomTree and LMTare the best predictive performance classifiers that come out in
top in this analysis. Future work will focus on the combination of best classification techniques that
can be used to improve the performance.
IX. ACKNOWLEDGEMENTS
I would like to thank my supervisor and external guide to their valuable suggestions and tips
to write this paper.
REFERENCES
1. http://www.tutorialspoint.com/data mining/dm_classification_prediction.htm
2. Fabricio Voznika, Leoardo Viana, Data Mining Classifications.
3. Lilla Gula, Robin Norberg Information Data Management for the Future of communication,
2013.
4. http://weka.sourceforge.net/

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5. Ghazi Johnny, Interactive KDD System for Fast Mining Association Rules. Date of Lecturer/
Staff Developing Center, Acceptance 8/6/2009.
6. Dr.Philip Gordon, Data Mining: Predicting tipping points, 2013.
7. Deepali Kharche, K. Rajeswari, Deepa Abin, SASTRA University, Comparison of different
datasets using various classification techniques with WEKA, Vol. 3, Issue. 4, April 2014.
8. Shravan Vishwanathan and Thirunavukkarasu K, “Performance Analysis of Learning and
Classification Algorithms” International journal of Computer Engineering & Technology
(IJCET), Volume 5, Issue 4, 2014, pp. 138 - 149, ISSN Print: 0976 – 6367, ISSN Online:
0976 – 6375.
9. Prof. Sindhu P Menon and Dr. Nagaratna P Hegde, “Research on Classification Algorithms
and Its Impact on web Mining” International journal of Computer Engineering & Technology
0976 – 6375.
10. Nitin Mohan Sharma and Kunwar Pal, “Implementation of Decision Tree Algorithm After
Clustering Through Weka” International journal of Computer Engineering & Technology
0976 – 6375.
AUTHORS DETAILS
Firas Mohammed Ali He received his BSc in computer science in 2010 from
Sirte University. He currently pursuing Master in Information Technology from
The Libyan Academy. His research area is Data Mining and Artificial
intelligence.
Dr. Prof.El-BahlulEmhemedFgee He received his PhD. in Internetworking,
Department of Engineering Mathematics and Internetworking in 2006 from
Dalhousie University, Halifax NS. Dr.Fgee Supervise students in Network Design
and Management .He Worked as the Dean of Gharyan High Institute of
Vocational Studies from 2008 to 2012 .and published many researches and
technical reports in international journals and conference proceedings.
Dr. Prof. ZakariaSulimanZubi He received his Ph.D. in Computer Science in
2002 from Debrecen University in Hungary he is an Associate Professor since
2010. Dr. Zubi, served his university under various administrative positions
including the Head of Computer Science Department 2003-2005. He published
as authors and a co-author in many researches and technical reports in local and
international journals and conference proceedings.

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