An Efficient PSO Based Ensemble Classification Model on High Dimensional Datasets

International Journal on Soft Computing (IJSC) Vol.8, No. 3/4, November 2017
DOI:10.5121/ijsc.2017.8401 1
AN EFFICIENT PSO BASED ENSEMBLE
CLASSIFICATION MODEL ON HIGH DIMENSIONAL
DATASETS
G. Lalitha Kumari1
and N. Naga Malleswara Rao2
1
Research Scholar, Acharya Nagarjuna University, Guntur, AP, India
2
Professor, Dept. of IT, RVR & JC College of Engineering, Guntur, AP, India
Abstract
As the size of the biomedical databases are growing day by day, finding an essential features in the disease
prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to
analyze, predict and interpret the feature information using the traditional feature selection based
classification models. Most of the traditional feature selection based classification algorithms have
computational issues such as dimension reduction, uncertainty and class imbalance on microarray
datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high
efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle
swarm optimization (PSO) based Ensemble classification model was developed on high dimensional micro-
array datasets. Experimental results proved that the proposed model has high computational efficiency
compared to the traditional feature selection based classification models in terms of accuracy , true
positive rate and error rate are concerned.
Keywords
PSO, Neural network, Ensemble classification, High dimension dataset.
1. INTRODUCTION
Feature selection is an important and essential technique used in data filtering for improving
machine learning models on large databases. Different feature selection models have been
implemented in the literature, each of them uses a feature evaluation measure to extract feature
subsets on the same databases. Learning classification models with all the high dimensional
features may result serious issues such as performance and scalability. Feature selection is the
process of selecting subset of features so that the original feature space is reduced using the
selection measures. Feature selection measures can be categorized into three types: wrappers,
filters and embedded models.
Extreme learning has wide range of applications in different domains like face recognition,
human action recognition, landmark recognition and protein sequence classification, medical
disease prediction [1]. Extreme Learning Machine can be defined as a single-hidden layer feed-
forward neural network (SLFN) with learning model [2-4]. The traditional optimization
approaches like gradient descent based back-propagation [5] evaluate weights and biases. The
proposed technique is responsible for decreasing the training time effectively through random
assignment of weights and biases. The above extended EL method results better efficiency and
performance as compared to all traditional approaches. But, EL has two major issues, those are:-

2
1)This model has over fitting problem and the performance cannot be predicted for unknown
datasets. 2) This model is not applicable to binary classification and uncertain datasets.
Feed-forward neural network can be considered as most commonly and widely implemented
neural network. The method has one or more hidden layer(s) along with an output layer. The
output layer is responsible for transmitting final response on the training dataset [6]. A large
number of research works have been implemented in the field of Feed-Forward Neural Networks
since years. This model has complex linear or nonlinear structure directly mapping from inputs.
These structures are not appropriate for classical parametric constraints to manage large inputs in
the traditional models. Another important feature of feed-forward neural network is the inter-
dependency among the layers through parameter mapping. Single-Hidden-Layer Feed-Forward
Networks (SLFNs) are treated as the most efficient and widely used feed-forward neural networks
on small datasets.
In order to resolve the issues of traditional EL, weighted-EL approach is developed subsequently.
The weights are increased gradually with respect to time in case of large sample size. In most of
the feed forward ANN techniques, parameters of each and every layer is required to be tuned
through several learning approaches. Gradient Descent-Based Approaches and Back-Propagation
(BP) techniques are some important learning algorithms in feed forward neural networks [7]. The
speed of learning models is very slow in case of feed forward neural networks compared to ANN.
Because of better generalization capability and fast computational speed, EL approach is named
as 'Extreme Learning Machine' (EL). A lot of problems are detected in case of conventional
Gradient-Descent Algorithms like stopping criterion, learning rate, number of epochs and local
minima [9].
The process of feature extraction has high significance in the field of classification. All features
are divided into two groups, those are:-
1) According to the first group, features extraction using noisy attributes and contextual
information.
2) The second group contains correlated features.
Traditional feature extraction models discard noisy features in order to decrease the high
dimensional features to a lower dimensional feature. Let us assume Nmin and Nmax are
minimum and maximum numbers of hidden neurons respectively, where N denotes the present
value of hidden neurons. For each and every N, the average accuracy rate of EL through 10-fold
cross-validation scheme is evaluated. At last, hidden neurons having maximum average accuracy
is chosen as optimal. After selecting the optimal numbers of hidden neurons, the EL classifier is
implemented in order to evaluate the classification accuracy by considering the outcomes of
principle component analysis(PCA) and the outcomes are averaged later.
The training datasets used in this paper have a significant issue for any classification models as
they have large number of feature space, ranging from 100 to 12000 features. The larger the
feature space increases the search space and computational memory for disease prediction.
Another crucial issue for handling the high dimensional features is the small sample size problem.
The accuracy of the model employed will be reduced if the size of the training data is not
sufficient relative to the feature space.
In the past, machine learning models used a single classification model to predict the test data
using the training samples. However, multiple classifiers can be used to predict the same test data
using the training samples. This process is known as ensemble learning. Ensemble classification

3
has been successfully applied to different classification problems to improve the classification
accuracy using the optimal feature selection measures.
Particle swarm optimization (PSO) is very popular optimization techniques in machine learning
models. PSO is generally applied in the literature to adjust the initialization parameters of base
classifiers in the ensemble learning models. The main objective of this paper is to optimize the
traditional PSO parameters in the ensemble classification model in order to improve the accuracy
and error rate. In the ensemble model, neural network is used as one of the base classifier and
weights are initialized using the proposed PSO technique.
The main contribution to this paper includes:
• A novel PSO based ensemble model is usually designed and implemented to improve the
overall classification true positive rate on high dimensional feature selection.
• Ensemble learning model is constructed from a group of base classifiers to predict the
high dimensional class labels. Here, search space of the traditional PSO model is
optimized using the proposed measures such as optimized fitness measure and chaos
gauss based randomization measure.
2. RELATED WORKS
Medical data prediction is the most important and complicated requirement in recent era. Many
approaches are developed in the field of medical disease prediction such as medical disease
prediction. In [9], association rule mining approach is integrated with multi-layer
perceptron(MLP) and back propagation approaches in order to predict and detect the chances of
breast cancer. Modular neural network is basically implemented to recognise and analyse cardiac
diseases using Gravitational Search Algorithm (GSA) and fuzzy logic embedded algorithm for
better performance [3]. With the exponential growth of information technologies, data mining
approaches are implemented in various domains such as biomedical applications and disease
prediction. Special emphasis can be given in not only detecting cancer, but also predicting the
disease in an early stage [6].
According to [1], gene selection technique is used to enhance the performance of the classifier
through the detection of gene expression subsets. Among most widely implemented gene
selection approaches, some are given below:- principal component analysis [4], singular value
decomposition [5], independent component analysis [6], genetic algorithm (GA) [7], and
recursive feature elimination method [8, 9].A micro-array feature selection technique is generally
applied to the pre-existing approaches for pattern classification. It not only decreases the
influence of noise, but also reduces the error rate in medical datasets [4-6].
[10-12] Implemented a PSO based spectral filtering model to high dimensional features of the
original training data. Two reconstruction methods are used, one is the principle component
analysis and the other is Maximum likelihood estimation. Several distribution algorithms were
used in randomization models. In most of these techniques Bayesian analysis is used to predict
the original data distribution using the randomization operator and the randomization data.
Feature selection is an important step in defect prediction process and has been extensively
studied in the field of machine learning. [10-13] Investigated different feature selection models
that represent ranked list of features and applied them to UCI repository datasets. They concluded
that wrapper is best model for limited data and limited feature sets. Software defect prediction
models are commonly classified in the literature as Decision tree models, Support vector machine
models, artificial neural network models and Bayesian models are summarized in the table 1.

4
Table 1: Overview of Traditional Classification Models on High dimensional Datasets
Classification Model Imbalance and
High
dimensionality
Property
Advantages Disadvantages
Decision Trees[5] Affected Robust to noisy data;
and decision rules
evaluation.
1)Prone to over-fitting.
2)Performance issue under
imbalanced property.
Artificial neural
networks
[7]
Affected Able to learn non-linear
functions. Robust
against errors.
1) Difficult to interpret
results.
2) Slow training and
prediction process.
Ensemble Models
[9-10]
Affected Best model for high
dimensional datasets
with complex feature
selection models
1)Fast processing
2)Low performance under
imbalanced data and
missing values.
Feature selection +
wrapper method[11] Highly Affected
The wrapper feature
selection approach is
useful in identifying
informative feature
subsets from high-
dimensional datasets.
Used traditional
algorithms such as
C4.5, KNN, Random
forest..
Limitations:
Accuracy is computed on
the subset of features.
Considers only less
number of features for
decision making patterns.
In the microarray datasets,
subset of features failed to
give affective decision
making patterns due to
insufficient gene patterns.
In order to handle feature
subsets (>100), single
classifier failed to perform
efficient results due to
memory constraints.
Ensemble classifier+
Biomedicine Field
affected Ensemble methods
proved to be superior to
individual classification
method for high
dimensional datasets.
The performance of the
classification need to
improve much.Features are
transformed into new ones
hence loss of originality. It
leads to overfitting of data.
Enhancing Ensemble
on Tweet Sentiment
Data
Affected Supports high
dimensionality upto
200 features..
Implemented existing base
classifiers for ensemble
model such as C4.5,
NB,SVM ,LR.Greatly
affects the Accuracy.

5
3. PROPOSED MODEL
Proposed PSO based ensemble classifier is a multi-objective technique which finds the local and
global optimum solutions by iteratively searching in a high dimensional space. Traditionally, as
the size of the training dataset is small, medical disease prediction rate could be dramatically
reduced due to class imbalance and high dimension space. In this filtering method, each attribute
is tested for missing values. Most of the traditional methods are capable of extracting missing
values from training data that store numeric attributes. Filtering makes models more accurate and
faster. If the attribute is numerical and the value is null then it is replaced with computed value of
equation (1). Also, if the attribute is nominal and has missing values then it is replaced with
computed value of equation (2). Finally, if the class is numeric then it is converted to nominal.
Proposed PSO based ensemble model is usually designed and implemented to improve the overall
classification true positive rate on high dimensional feature selection. Generally, ensemble
learning model is constructed from a group of base classifiers to predict the high dimensional
class labels. Here, search space of the traditional PSO model is optimized using the proposed
measures such as optimized fitness measure and chaos gauss based randomization measure. In
our model, different base classifiers such as decision tree, Naïve Bayes, FFNN are used to test the
performance of proposed model to the traditional models.
Proposed Improved PSO based Ensemble Classification Algorithm:
Step 1: Data pre-processing on Training high dimensional data.
Load dataset 1 2 n
HD ,H D ....H D
For each attribute A(i) in the 1 2 n
HD ,H D ....H D
do
For each instance value I(j) in the A(i)
do
if(isNum(I(j)) && I(j)==null)
then
( ) ( ) ( ) ( )
n
A( j ) A( j ) A( j )
j 1/i j
I I I
j (| I(j) )
I |) / (Max Min )
= ≠
= − µ −
∑ ----(1)
end if
if(isNominal(Ai) && Ai(I)==null)
then

6
Figure 1: Proposed Model
( )
j Prior Pr ob(A(i),class ;
I (m))
= ---(2)
Here, mth class of the missing value is used to find the prior probability in place of missing value
end if
End for
Step 2: // improved PSO for optimal feature selection
a) Initializing particles with feature space, number of iterations, velocity, number of
particles etc.
b) Compute hybrid velocity and position for each particle in ‘d’ dimensions using the
following equations

7
chaos1 i chaos2 i
2
chaos1 chaos2 chaos1 chaos2 chaos1 chaos2
v(d 1,i) .[ (d,i).v(d,i). (pBest X(d,i)) (gBest X(d,i))]
X(d 1,i) X(d,i) v(d 1,i)
is the convergence factor computed as
2
| 2 ( ) ( ) 4( ) |
+ = ψ ω θ − + θ −
+ = + +
ψ
ψ =
− θ + θ − θ + θ − θ + θ
chaos1 chaos2
------(3)
where , Ortho chaos gauss randomization
θ θ ∈
In this optimized model, inertia weight is computed as
max current max max min
max
min
max
(d,i) (I / I ).( )
: max inertia
: min inertia
I : max iteration
ω = ω − ω − ω
ω
ω
Step 3: Computing fitness value using ortho chaotic gauss randomization measure.
In this proposed PSO model, a random value between 0 to 1 is selected using the following
equation as
2
X
2
X
(X )
k k
i j j
X
k
j
1
R max{ . (1 ), e } -------(4)
2
K 1,2...iterations
(0,1)
−µ
−
σ
= µβ −β
πσ
=
β ∈
Proposed feature selection fitness measure is given as
|F|
i
i 1
i 1 i 2
f
1 2 i
i
f
i
f
Fitness w .acc w .(1 )
N
where w ,w R
f is the flag value 1 or 0 . '1' represents selectd feature , '0' non-selected feature.
N represents number of features.
acc : accuracy of the selecte
=
= + −
∈
∑
d features in the ith iteration
Step 4: Apply ensemble classification model with accuracy acci on the selected features in the ith
iteration. Here, Decision trees, NB, FFNN are used as base classifiers for high dimensional
classification.
Step 5: For each particle compute its fitness value and compute classification accuracy in the
previous step.
Step 6: Update particle velocity, position, global best and particle best according to the fitness
value conditions as shown in figure 1.
Step 7: This process is continuous until max iteration is reached. Otherwise go to step 2.

8
4. EXPERIMENTAL RESULTS
In this section, we performed experiments using proposed model on well known microarray
datasets and compared the statistical performance with traditional models. Dataset [15] used for
experimental evaluation is listed in Table 2. Proposed model was implemented in the java
environment with Netbeans IDE. From these experimental results, 10% of the training instances
are used as test data for cross validation and performance analysis.
From the experimental results, it is clear that optimizing PSO in the base classifiers improves
classification rate along with true positive and true negative rate. Also, the main advantage of
using proposed model is to reduce the error rate on high dimensional features.
Table 2: Datasets and its characteristics
Micro array Datasets Attributes Size Data-Type
lung-Michigan 7000 Continuous/Numeric
lungCancer_train 12000 Continuous/Numeric
DLBCL-Stanford 4000 Continuous/Numeric
Proposed ensemble methods increase the performance of true positive rate and accuracy on entire
high dimensional datasets. Proposed model uses the entire training data set for construction of
decision patterns; therefore the prediction accuracy of each cross validation tends to be more
accurate than the traditional ensemble classification models.
True negative measure evaluates the ratio of number of instances that are not affected with the
cancer disease, which are identified correctly.
True positive measure defines the ratio of number of cancer disease instances that have been
predicted as positive rate.
Mean Absolute error Error: Average misclassification rate of each test data in the cross
validation.
Precision measure computes the ratio of correctly predicted cancer instances among the entire
disease affected instances.
Gene based cancer disease patterns using proposed feature selection based ensemble learning
model on ovarian cancer dataset.
Table 3, describes the optimization of improved PSO of individual weak classifiers using the
ensemble classifier. Here, the performance evaluation are performed in terms of statistical
analysis, true positive rate and error rate on ovarian dataset. From the table, it is clearly observed
that the proposed optimization model has high computational efficiency compared to traditional
classification models.
Table 3: Performance analyses of classification accuracy on ovarian dataset on 10 cross validation.
10 % test data Ovarian Dataset
Model
True
positive
Error
Rate
Run
time(ms)
PSO+NaiveBayes 0.87 0.2492 4526
PSO+RandomForest 0.8923 0.2293 4297
PSO+Neuralnetwork 0.9055 0.1987 3975
IPSO-Ensemble 0.9575 0.1314 3297

9
Figure 3, describes the optimization of improved PSO of individual weak classifiers using the
ensemble classifier in graphical chart. Here, the performance evaluations are performed in terms
of statistical analysis, true positive rate and error rate on ovarian dataset. From the table, it is
clearly observed that the proposed optimization model has high computational efficiency
compared to traditional classification models.
Figure 3: Performance of ovarian cancer dataset using IPSO ensemble classification model with existing
models.
Table 4 and Figure 4 describe the performance of the improved PSO algorithm with ensemble
classification to the traditional models on microarray cancer datasets. From the table, it is clearly
observed that the proposed model has high accuracy and less error rate compare to traditional
models in terms of error rate, accuracy and runtime.
Table 4: Performance analysis of proposed model to the traditional models on three microarray datasets.
Datasets PSO+NaiveBayes PSO+C4.5 PSO+FFNN IPSO+Ensemble
Lung Cancer 0.697 0.8084 0.8525 0.9374
Lung Michigan 0.7935 0.8274 0.845 0.9153
Lymphoma 0.8153 0.8253 0.8574 0.9646
ErrorRate 0.364 0.304 0.294 0.225
Runtime(ms) 7351 6253 6364 5254
0
0.2
0.4
0.6
0.8
1
1.2
Performance
Analysis
Models
TruePositive
Error Rate

10
0
0.2
0.4
0.6
0.8
1
1.2
Analysis
Models
Lung Cancer
Lung Michigan
Lymphoma
ErrorRate
Figure 4 : Performance of proposed model to traditional models in terms of accuracy, error rate and run-
time.
5. CONCLUSION
In this paper, an optimized PSO based feature selection method is integrated with the ensemble
model on microarray datasets. Most of the traditional feature selection based classification
algorithms have computational issues such as dimension reduction, uncertainty and class
imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme
learning machine due to its high efficiency, the fast processing speed for real-time applications.
Experimental results proved that the proposed model has high computational efficiency compared
to the traditional feature selection based classification models in terms of accuracy, true positive
rate and error rate are concerned.
REFERENCES
[1] Y. Yan, Q. Zhu, M. Shyu, and S. Chen, “A Classifier Ensemble Framework for Multimedia Big Data
Classification”, “IEEE 17th International Conference on Information Reuse and Integration”, pp. 615-
622, 2016.
[2] I. Triguero, M. Galar, S. Vluymans, C. Cornelis, H. Bustince, F. Herrera and Y. Saeys, “Evolutionary
Undersampling for Imbalanced Big Data Classification “,pp.715-722, 2015.
[3] I. Triguero, M. Galar, D. Merino, J. Maillo, H. Bustince, F. Herrera, “Evolutionary Undersampling for
Extremely Imbalanced Big Data Classification under Apache Spark”, “IEEE Congress on
Evolutionary Computation (CEC)”, pp.640-647, 2016.
[4] M. Popescu and J. M. Keller, “Random projections fuzzy k-nearest neighbor(RPFKNN) for big data
classification “, “IEEE International Conference on Fuzzy Systems (FUZZ)”,pp.1813-1817, 2016.
[5] R. Pakdel and J. Herbert, “Efficient Cloud-Based Framework for Big Data Classification”, “IEEE
Second International Conference on Big Data Computing Service and Applications “, pp.195-201,
2016.
[6] C. Lemnaru, M. Cuibus, A. Bona, A. Alic and R. Potolea, “A Distributed Methodology for
Imbalanced Classification Problems“, “11th International Symposium on Parallel and Distributed
Computing “, pp. 164-171, 2012.
[7] X. R. Jenifer and R. Lawrance, “An Adaptive Classification Model for Microarray Analysis using Big
Data”, 2016.

11
[8] B. Jaison, A. Chilambuchelvan and K. A. Junaid, “Hybrid Classification Techniques for Microarray
Data “,”Springer Natl. Acad. Sci. Lett. “.
[9] N. C. Salvatore, A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M.
Morelli, M. C. Gilardi and A. Quattrone, “Machine learning on brain MRI data for differential
diagnosis of Parkinson’s disease and Progressive Supranuclear Palsy”, “Journal of Neuroscience
Methods “, pp.230– 237, 2014.
[10] M. K. Shahsavari, H. R. Bakhsh and H. Rashidi, “Efficient Classification of Parkinson’s Disease
Using Extreme Learning Machine and Hybrid Particle Swarm Optimization”, “4th International
Conference on Control, Instrumentation, and Automation (ICCIA) 27-28 January 2016, Qazvin
Islamic Azad University, Qazvin, Iran”, pp.148-154, 2016.
[11] C. Lemnaru, M. Cuibus, A. Bona, A. Alic and R. Potolea, “A Distributed Methodology for
Imbalanced Classification Problems“, “11th International Symposium on Parallel and Distributed
Computing “, pp. 164-171, 2012.
[12] X. R. Jenifer and R. Lawrance, “An Adaptive Classification Model for Microarray Analysis using Big
Data”, 2016.
[13] B. Jaison, A. Chilambuchelvan and K. A. Junaid, “Hybrid Classification Techniques for Microarray
Data “,”Springer Natl. Acad. Sci. Lett. “.
[14] A. Govada, V. S. Thomas, I. Samal and S. K. Sahay, “Distributed multi-class rule based classification
using RIPPER”, “IEEE International Conference on Computer and Information Technology”, pp.303-
309, 2016.
[15] http://datam.i2r.astar.edu.sg/datasets/krbd/index.html
AUTHORS
Lalitha Kumari Gaddala, B.Tech,M.Tech,has a work experience of 12 years. She is
pursuing her Ph.D in Computer Science and Engineering under Acharya Nagarjuna
University, Guntur and Andhra Pradesh. She is working as Senior Assistant Professor in
Prasad V Potluri Siddhartha Institute of Technology, Vijayawada. Her research interests
are Soft Computing-Optimization algorithms, Machine Learning. She has published few
papers in International conferences and International Journals.
Dr. N. Naga MalleswaraRao,B.Tech, M.Tech and Ph.D, has a work experience of 25
years and 9 Months. He is working as Professor, Department of IT, RVR& JC College
of Engineering, Guntur (Dt). His research interests are Computer Algorithms,
Compilers, and Image Processing. He has published few papers in International
conferences, National Conferences and International Journals.

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