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IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
Classification Model Using Optimization
Technique: A Review
1
1
Deoshree Diwathe, 2 Snehlata S.Dongare
M. Tech Computer Science & Engineering, G. H. Raisoni College of Engineering,
Nagpur, India
2
Computer Science and Engineering, G. H. Raisoni College of Engineering,
Nagpur, India
Abstract - Data Mining is widely used in Sciences and technology fields. Classification is essential and important
technique in data mining. Classification technique contains different types of classifiers. Decision Tree is most useful ruled
based classifier, the rules are in the form of IF-THEN rules and generate it according to applicable conditions in tree
structure, and checks all condition for classifying the data. The research of this project is that, Decision Tree is designing by
greedy approach which is used to generate decision for each and every attributes, but the demerits of classification
technique is generating number of rules during classification, it tends to less accuracy and efficiency. Resolved this
disadvantage with the help of Artificial Bee Colony Optimization Algorithm. It is used to optimize rules and update the
conditions during classification and optimized result. Therefore, classification using optimization algorithm is increasing
accuracy and efficiency of classification model.
Keywords - Data Mining, Classification technique, Decision Tree Classifier, Artificial Bee Colony Optimization Algorithm(ABC
Optimization Algorithm) .
1. Introduction
In classification technique, analyzing a huge number of
data and produce a collection of grouping rules and it is
used to classify future data or predict data. Classification
technique is work on the labeled datasets and it is not work
on the real datasets. Classification models are very helpful
to predict classified or categorized classes labels.
Classification Technique is supervised learning in which
Teacher is present to train that data and divides that data
into different classes. Classes are created by function of
attributes in dataset. Classification method’s result is
depending on the “yes” or “no” answer. In Data Mining,
classification process develops a classified model for each
class in a database, depend on the features and properties
available in a collection of class-labeled which is training
data. The derived model is created with the help of
analysis of a set or collection of training data. Firstly, the
classification algorithms are help to build the classifier for
categorized the datasets. The classifier is creating by the
training dataset and generates the number of classes
according to their function and classes labels. The training
data or samples are referred as attributes. The test data is
utilized to determine the accuracy and also checks the
efficiency of classification models or rules. The
classification model is used for other purpose or applied on
the new system if the accuracy is acceptable. There are
many data classification methods such that including
D
ata mining is defined as; extract required or useful
data from bulk of datasets. So that, it consists
multiple collection and managing data and it also
consists analysis of data and prediction on data. This
performance uses Classification Models of Data Mining
techniques. Data Mining is one of the most important
computational process for discovery the patterns, text,
forecasting, prediction and many more from the huge
datasets. There are so many methods at the intersection of
artificial intelligence such as neural network, machine
learning such as supervised and unsupervised learning,
static or database system. Overall aim of the Data Mining
process is to extract information from a number of dataset
and transform into grid structure according to their
attributes. Data mining is predictive in nature which is
used to predict the patterns and the final prediction has
shown in the form of graphical view. Data mining delivers
the methodology and technology to promote these huge
data into useful and easy information for decision making.
Data mining is a process of gather knowledge from such
immense of data. There are different types of techniques in
data mining such as classification, clustering, association.
In this project, more focused on classification technique.
42
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved.
IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
complex problem during classification. That’s why the
classification accuracy for unlabeled dataset is decreases.
So this is the main disadvantage of SVM classifier
algorithm. Therefore, a Multiobjective Genetic SVM
Approach i.e. classification with Genetic Algorithm which
is used for inflating the selected training set and it is used
to find out the tricky problems and solve that problem in
optimal way. Finally it created perfect result [2].
decision-tree methods (IF-THEN rules), such as C4.5
algorithm, ID3 algorithm and neural networks.
Classification technique in data mining contains some
disadvantages like decision tree generates number of rules
during classification of data. Therefore accuracy and
efficiency is decreases. To remove this drawback by using
Artificial
Bee
Colony Optimization
algorithm.
Optimization Algorithm is used to find out the optimal
solution and gives optimal output of classification.
Optimization algorithm is manipulating to diminish the
efforts and time. Also it is deploy to compress the number
of rules which is formed by Decision Tree Classifier
during classification and it tends to decrease accuracy and
efficiency. Within the short time period, it gives optimal
solution and increases accuracy and efficiency.
In this research paper, artificial neural network (ANN) is
used for classification purpose and Artificial Bee Colony
optimization algorithm is applicable for removing the
drawbacks in classification algorithm. Artificial neural
network is not sufficient to generate robust application for
classification using ANN. This algorithm does not
generate reduced design of the ANN. So this is big
drawback of artificial neural network algorithm. Artificial
Bee Colony is manipulate to resolve that problem with the
help of weight Updating .This algorithm is used to
maximize the accuracy and minimize the complexity i.e.
number of connection of the ANN is decreases and
problem is easily solved [3].
2. Literature Review
In Classification of Data Mining, There are analyzing
different kind of data mining techniques introduced in
recent years for Classification. The following information
shows different data mining techniques used in the
Classification over different datasets. The unique concept
is classifying different dataset using Optimization
Algorithm and increase their efficiency and accuracy. Also
there are different types of optimization algorithm to
classify data in optimized way and gives optimal solution.
In this paper, C4.5 algorithm of decision tree is used for
classification purpose and Ant colony optimization is for
optimized the problem related with classification
algorithm. In C4.5 algorithm, when classification is
applies on training data at that time all training cases are
organized in the form of IF-THEN rule and the number list
is rapidly increases. The goal of this paper is,
•
•
In this Research paper, Population is very important
factor which can be provide important information to
create the decision such as economic, business, marketing
etc. and it is protecting the population from various harm.
Real world dataset of fire evacuation is very sparse and
dataset is also noisy. Different dangerous task, requirement
and environment are varying differently. So there are
many inconsistencies in dataset. That’s why, its
classification result is inaccurate or misleading and
response time is very limited. Therefore Classification
model contains some drawbacks.
Particle swarm optimization algorithm for population
classification in fire evacuation is used to remove those
drawbacks and optimized the recall measures and
conditions related to population classification. In this
research paper, classification with the help of Particle
Swarm Optimization Algorithm organize to design
effective method for encoding classification rules, and use
an encyclopedic (cover all) learning strategy for including
particles and managing diversity of the swarm. This
proposed system is comparatively better than the
classification model and it is especially very easily work
on the real world dataset. Also this hybrid model is
working anywhere i.e. Multiobjective Classification Model
using Particle Swarm Optimization [4].
In this paper, LSC means Land use suitability
classification is useful to classify specific areas of
land depend upon their suitability which is useful
for agriculture.
Maximized efficiency and optimized complexity
using Ant Colony Optimization.
In this research paper, Author applied put forward
algorithm i.e. Ant-Miner. Ant- Miner was used to improve
the system performance or expand the applicability and
usability of Ant-Miner which is used to handle non-spatial
problems [1].
This paper proposed a methodology called Ant-Miner.
Ant-Miner is combining model of the Ant Colony
Optimization concepts and some classification concepts.
Decision tree Classifier or CN2 algorithm is used for
classifying the dataset. CN2 algorithm is based on the IFTHEN conditions. CN2 algorithm creates number of rule
In this research paper, Support Vector Machine (SVM)
classifier is used to classify the limited set of unlabeled
data and genetic algorithm is used for optimization
algorithm. In this paper, SVM classifier is one of the
strong classifier but it is not solution for tricky and
43
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved.
IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
declared that the fuzzy genetic algorithm with the fitness
output based on data mining shown comparatively better
outcome to other fitness result but in case of classification
technique Naïve Bayesian algorithm is more reliable [6].
list during classification and therefore the accuracy and
efficiency of that proposed method was decreases. Ant
Colony Optimization Algorithm is used to solve the
problem is easy way. The objective or goal of Ant- Miner
is to extract some data from the huge dataset .Ant Miner is
based on the behavior of real ant’s colonies and their
principles and also some data mining concepts and
principles. In this research paper, Ant-Miner is compared
with the CN2 algorithm for classification. So the
comparative results as follows:•
•
•
In this research paper, k-Nearest Neighbors (kNN)
algorithm is used for classification. In this method, to
detect the nearest points in a dataset from the required
points. The outcome of kNN is used for regression and
classification. Those methods are widely used in both
machine learning and data mining. Nearest-Neighbors
framework methods are successful on different types of
pattern classification drawbacks. In kNN method, a group
of frameworks has to be determined that accurately
presents the input patterns. Then the work of classifier has
to be assigns number of classes according to nearest point
in this group.
Ant-Miner is better than the CN2 algorithm because,
the accuracy of Ant-Miner is high and it predicted
accurate result.
Ant-Miner created smaller or simpler rule lists than
the CN2 algorithm.
The metaheuristic approach has both robust and
versatile.
In this research paper, Particle Swarm Optimization
Algorithm is manipulate to create the optimal result. The
main goals of Particle Swarm Optimization are as
follows:-
The hybrid concept of optimization algorithm and
classification algorithm i.e. Ant-Miner is very effective for
prediction in data mining [5].
In this research paper, the soft computing knowledge or
techniques have widely used in machine learning method.
Fuzzy logic or fuzzy ruled based evolutionary algorithm
created IF-THEN rules. This fuzzy model related with
ruled based system is paired with the optimization
algorithm. Fuzzy Logic or neural network is used for
classification purpose and classification is not work on the
real dataset, it is only work on the labeled dataset. That’s
the big problem of classification algorithm. Therefore
optimization algorithm is used to solve this problem with
fuzzy logic method. Fuzzy rule based method is having
following drawbacks:
•
•
•
•
•
Frstly use the standard (PSO) optimization algorithm
i.e. particle swarm optimization algorithm to find out
those nearest point from that group.
Second, Generated a hybrid new algorithm is called as
adaptive Michigan PSO (AMPSO) which is used to
reduce the measurement of the search space and
AMPSO provides more flexibility than standard PSO
algorithm.
In this paper, comparatively the output of the standard PSO
algorithm and hybrid AMPSO algorithm is applied on
different benchmark datasets and find out that AMPSO
hybrid algorithm is always found a better result than the
standard PSO. It was also able to improve the results of the
k-Nearest Neighbor algorithm [7].
Fuzzy ruled based method created number of
significant or continuous fuzzy rule lists for
multivariate dataset of classification.
Fitness of system is decreases because of
generating the number of rule lists.
Fuzzy logic worked on the labeled dataset only
not on the real dataset.
In this research paper, nowadays, Prediction of spinal cord
disorders is very much complex task because it requires
knowledgeable and an experienced radiologist to detect the
problems through images of MRI i.e. Magnetic Resonance
Imaging. The CAD i.e. Computer Aided Diagnosis system
is very useful to help the radiologist for detection of
abnormalities in the spinal cord more optimal way using
optimization algorithm. In the vertebral column, the
required dataset which is available in three classes that
shows the condition of the spinal cord. The three classes
are herniated disk class, spondylolisthesis class and normal
class. Datasets contains number of classes. There are some
major problems called class imbalance in classification.
This problem tends to cause a lack of accuracy in the
classification results. In this research, the hybrid model of
genetic algorithm and bagging technique are introduced to
Genetic Algorithm is used for optimization algorithm and
Naives Bayesian is used for classification method. The
proposed hybrid approach is Fuzzy Genetic algorithm and
it is compared with the Naives Bayesian algorithm.
The interpretation of Genetic algorithm related with Fuzzy
having better fitness value result which is differentiate to
the interpretation of Naïve Bayesian algorithm to
accurately and perfectly determine and classify patterns.
Interpretation analysis is based on testing on both trained
dataset as well as tested dataset. That dataset which is
created from given or original dataset. It is directly
44
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved.
IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
experimental results shows that, with the help of using 40
popular benchmark datasets for checking the accuracy of
the proposed system and identify different quality
functions that improve the some quality function that are
already used in Ant-Tree- MinerM[11].
improve the accuracy of classification related with spinal
cord disorders. Genetic algorithm is used for selecting the
genes and bagging technique is used to resolve the
complex problem of class imbalance. The hybrid method
is applied on three classification algorithms such as naïve
bayes, neural networks and k-nearest neighbor. The result
shows that, the hybrid method is used to improve the
classification of spinal cord disorders for classifier
algorithms [8].
3. System Architecture
In this research paper, classification technique in data
mining is supervised learning process which is very useful
in Artificial Neural Network (ANN). The ANN is having
ability to accept the number of inputs and then it generate
a network with weights and input values and lastly perfect
output generated. ANN must have large amount of
framework or measures because it generates better
accuracy and efficiency of classification. Artificial Bee
Colony Optimization algorithm is manipulating to help to
increase the accuracy and efficiency in optimal way.
Artificial Bee Colony Optimization algorithm is having
capability of exploitation and exploration which is used to
update the weights and solving the problem of ANN
classifier in optimal way [9].
In this research paper, Back Propagation machine learning
algorithm in neural network is utilized for classification
purpose. Neural network classifier is having some
drawbacks as follows:• The weights of the input vectors are very high. That’s
why; accuracy of proposed model has been decreased.
• The proposed network was very complex for high
weights. Therefore, efficiency is low and maximized
errors.
Fig1:- Flowchart of Classification Model Using Optimization Technique
Artificial Bee Colony Optimization algorithm is easy to
update new weights and solve the problems of complexity
of the proposed network. This algorithm is used to
optimize the architecture of Neural Network and increases
the accuracy and efficiency [10].
4. Methodology
In classification technique, analyzing and classifying a set
of datasets and then it will generate a classification model.
Optimization Technique is used to reduce efforts for
solving complex problems and it gives optimal solution on
complex problem. Classification using optimization
technique is very effective and simple or easy to
understand. In this project, classification dataset is taken
from UCI repository benchmark dataset which is
mentioned in the literature review papers.
In this research paper, the presented system or method is
Ant-Tree-Miner model which is created with the help of
decision tree classification algorithm. The presented
system is hybrid combination of Ant Colony Optimization
(ACO) which is very popular meta- heuristic algorithm
and Decision tree classification algorithm. Ant-TreeMinerM is a unique System to introduce for enhancing the
Ant-Tree-Miner. This new approach is utilized to learn
multiple-tree classification models. A multiple-tree model
consists of number of decision trees and one for each class
value; where each class is depend upon the decision tree
which is responsible for decreasing its class value and all
other class values available in the class domain. Author’s
Using Literature Review, project work contains
classification algorithm that is Decision Tree classifier
(Top-Down approach or Greedy Approach) to classify the
dataset and Artificial Bee Colony is for Optimization
algorithm to remove the drawbacks of classification.
45
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved.
IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
4.1 Decision Tree Classifier
•
Decision Tree Classifier is very simple and effective
classifier to classify datasets in tree structure.
During dancing, employed bee spreads the
information related to the neighbor food source to
the onlooker bee.
2. Onlooker Bee:In Decision Tree algorithm, there are number of
conditions. If those conditions are true then generate next
condition and forward it onto the next level of decision
tree, otherwise if condition is false then stop that process at
that point.
•
•
Decision Tree algorithm is totally IF-THEN rule based
classifier. The rules are generated in IF-THEN rule based
form and according to rules, conditions are check.
Decision trees can be formed from number of rule sets, IFTHEN rule is denoted as following pattern:-
•
•
“IF attrA > x AND attrB <= y AND … THEN Class1”
3. Scout Bee:-
OR
Scout Bee is used to replace the abandoned food sources
are detected and are converted or replaced with the new
food sources.
“IF attrA < x AND attrB >= y AND … THEN Class2”
It is a Greedy Approach that is Top-To-Bottom approach
therefore the tree checks their condition from top to
bottom.
Firstly, classification task with the help of Artificial Bee
Colony Optimization Algorithm is introducing as follows
components:-
Greedy Approach is easy to design the decision tree and
easy way to plot the conditions.
o
Rule Format:The IF-THEN rules are categorized
into two parts. IF part is known as antecedent part
which is used for representing the conditions for
each and every attribute. THEN part is known as
consequent part which is useful for showing
classes and that classes depends on the features
and condition of attributes. Each and every
attributes has its low- bound i.e. lower value of
rule list and high-bound i.e. higher value of this
rule list.
o
Fitness Function:Fitness function is manipulated for
evaluating or determining the nectar amount of
classified that data. This fitness function is used
to calculate all features in the dataset. If the
calculating value of a features is between the
upper bound and lower bound, therefore the
features filled by the rule.
o
Search Strategy:-
4.2 Artificial Bee Colony Optimization Algorithm
Artificial Bee Colony optimization algorithm is inspired
from behavior of real Bee’s Foraging. In this algorithm,
System of communication of number of bees can also be
seen within the system. Artificial Bee Colony
Optimization algorithm collects information about
different parts of the environment.
There are 3 Types of Bees:1. Employed Bee
2. Onlooker Bee
3. Scout Bee
1. Employed Bee:•
•
•
•
Each Onlooker Bee watches the dance of
Employed Bees and gets information from dance
of the Employed Bee.
Now onlooker bee is converted into the
Employed bee and goes to find out the food
source.
Then choose neighbor food source from the dance
and go to that neighbor food source. After
choosing the neighbor food source, they detects
nectar amounts.
Then come back to the home after updating the
nectar amount.
Each Employed Bee is behaving like employed
such as they collect food from different area.
Employed bee goes to the outside and find out the
food source and determine the neighbor food
source.
Then, employed bee evaluates nectar amount of
food source.
Then, come back to home and dance in the hive.
Exchanged local Search strategy
is applicable for searching purpose. Search
46
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved.
IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
Strategy is consuming time to classified number
of dataset.
o
Rule Discovery:Classification work is carried out
with number of rules and that attributes are
categorized in different classes.
o
Rule Pruning:-
4. Conclusion
In this paper, Artificial Bee Colony Optimization
Algorithm is applicable for classification which is very
robust and efficient algorithm. An ABC algorithm is
useful for train the dataset and updates the weights,
because of this purpose it is very flexible in nature and
minimizes the number of rules created by decision tree
algorithm. Finally, it improves accuracy and efficiency.
Rule pruning is used to remove
for redundant features.
o
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Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved.
IJCSN International Journal of Computer Science and Network, Volume 6, Issue 1, February 2017
ISSN (Online) : 2277-5420
www.IJCSN.org
Impact Factor: 1.5
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