International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

STUDY OF Ε-SMOOTH SUPPORT VECTOR
REGRESSION AND COMPARISON WITH Ε- SUPPORT
VECTOR REGRESSION AND POTENTIAL SUPPORT
VECTOR MACHINES FOR PREDICTION FOR THE
ANTITUBERCULAR ACTIVITY OF OXAZOLINES AND
OXAZOLES DERIVATIVES
Doreswamy1 and Chanabasayya .M. Vastrad2
1

Department of Computer Science, MangaloreUniversity, Mangalagangotri-574 199,
Karnataka,INDIA
Doreswamyh@yahoo.com

2

Department of Computer Science, MangaloreUniversity, Mangalagangotri-574 199,
Karnataka, INDIA
channu.vastrad@gmail.com

ABSTRACT
A new smoothing method for solving ε -support vector regression (ε-SVR), tolerating a small error in
fitting a given data sets nonlinearly is proposed in this study. Which is a smooth unconstrained
optimization reformulation of the traditional linear programming associated with a ε-insensitive support
vector regression. We term this redeveloped problem as ε-smooth support vector regression (ε-SSVR).
The performance and predictive ability of ε-SSVR are investigated and compared with other methods
such as LIBSVM (ε-SVR) and
P-SVM methods. In the present study, two Oxazolines and Oxazoles
molecular descriptor data sets were evaluated. We demonstrate the merits of our algorithm in a series of
experiments. Primary experimental results illustrate that our proposed approach improves the
regression performance and the learning efficiency. In both studied cases, the predictive ability of the εSSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate
that ε-SSVR can be used as an alternative powerful modeling method for regression studies. The
experimental results show that the presented algorithm ε-SSVR, , plays better precisely and effectively
than LIBSVMand P-SVM in predicting antitubercular activity.

KEYWORDS
ε-SSVR , Newton-Armijo, LIBSVM, P-SVM

1.INTRODUCTION
The aim of this paper is supervised learning of real-valued functions. We study a sequence
S = x , y , . . . , x , y
of descriptor-target pairs, where the descriptors are vectors in ℝ
and the targets are real-valued scalars, yi ∈ ℝ.Our aim is to learn a function f: ℝ → ℝ which
serves a good closeness of the target values from their corresponding descriptor vectors. Such a
function is usually mentioned to as a regression function or a regressor for short.The main aimof
DOI : 10.5121/ijscai.2013.2204

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

regression problems is to find a function fx that can rightly predict the target values,y of new
input descriptor data points, x, by learning from the given training data set, S.Here, learning
from a given training dataset means finding a linear surface that accepts a small error in fitting
this training data set. Ignoring thevery smallerrors that fall within some acceptance, say εthat
maylead to a improvedgeneralization ability is performed bymake use of an ε -insensitive loss
function. As well as applying purpose of support vector machines (SVMs) [1-4], the function
fx is made as flat as achievable, in fitting the training data. This issue is called ε -support vector
regression (ε-SVR) and a descriptor data pointx ∈ R is called a support vector if"f#x $ − y " ≥
ε.Generally, ε-SVR is developed as a constrained minimization problem [5-6], especially, a
convex quadratic programming problem or a linear programming problem[7-9].Suchcreations
presents 2m more nonnegative variablesand 2m inequality constraints that increase the problem
sizeand could increase computational complexity for solvingthe problem. In our way, we change
the model marginally and apply the smooth methods that have been widely used for solving
important mathematical programming problems[10-14] and the support vector machine for
classification[15]to deal with the problem as an unconstrained minimizationproblemstraightly.
We name this reformulated problem as ε – smooth support vector regression(ε-SSVR). Because
ofthe limit less arrangement of distinguishability of the objectivefunction of our unconstrained
minimization problem, weuse a fast Newton-Armijo technique to deal with this reformulation.
It has been shown that the sequence achieved by the Newton-Armijo technique combines to the
unique solutionglobally and quadratically[15]. Taking benefit of ε-SSVR generation, we only
need to solve a system of linear equations iteratively instead of solving a convex quadratic
program or a linear program, as is the case with a conventionalε-SVR. Thus, we do not need to
use anysophisticated optimization package tosolve ε-SSVR. In order to deal with the case of
linear regression with aOxazolines and Oxazoles molecular descriptor dataset.
The proposed ε-SSVR model has strong mathematical properties, such as strong convexity and
infinitely often differentiability. To demonstrate the proposed ε-SSVR’s capability in solving
regression problems, we employ ε-SSVR to predict ant tuberculosis activity for Oxazolines and
Oxazoles agents. We also compared our ε-SSVR model with P-SVM[16-17] and LIBSVM [18]
in the aspect of prediction accuracies. The proposed ε-SSVR algorithm is implemented in
MATLAB.
A word about our representation and background material is given below. Entire vectors will be
column vectors by way of this paper.For a vector xin the n-dimensional real descriptor space
R , the plus functionx( is denoted as x( = max0, x , i = 1, … . . , n. The scalar(inner)
product of two vectors x and y in the n-dimensional real descriptor space R will be reprsented
by x, y and the p-norm of x will be represnted by ‖x‖. . For a matrix A ∈ R ⨯ , A is the iTh
row of A which is a row vector inR ? A column vector of ones of arbitrary dimension will be
reprsented by 1. For A ∈ R ⨯ and B ∈ R⨯2 , the kernel KA, B maps R ⨯ ⨯
R⨯2 intoR ⨯2 . In exact, if x andy are column vectors in R , then Kx , , y is a real number ,
,

KA, x = K#x , , A, $ is a column vector in R . andKA, A, is an m ⨯ m matrix . If f is a
real valued function interpreted on the n-dimensional real descriptor spaceR , the gradient of
f at x is represented by ∇fx which is a row vector in R and n ⨯ n Hessian matrixof second
partial derivatives of f at x is represented by∇5 fx . The base of the natural logarithm will be
represented bye.

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

2. MATERIALS AND ALGORITHAMS
2.1 The Data Set
The molecular descriptors of 100Oxazolines and Oxazoles derivatives [19-20] based H37Rv
inhibitors analyzed. These molecular descriptors are generated using Padel-Descriptor tool [21].
The dataset covers a diverse set of molecular descriptors with a wide range of inhibitory
activities against H37Rv. The pIC50 (observed biological activity) values range from -1 to 3.
The dataset can be arranged in data matrix. This data matrix x contains m samples (molecule
structures) in rows and n descriptors in columns. Vector y with order m × 1 denotes the
measured activity of interest i.e. pIC50. Before modeling, the dataset is scaled.

2.2 The Smooth ε –support vector regression(ε-SSVR)
We allow a given dataset Swhich consists of m points in n-dimensional real descriptor space R
denoted by the matrix A ∈ R ⨯ and m observations of real value associated with each
descriptor. That is,S = A , y | A ∈ R , y ∈ R, for i = 1, … … , m we would like to search a
nonlinear regression function,fx , accepting a small error in fitting this given data set. This
can be performed by make use of the ε- insensitive loss function that sets ε- insensitive “tube”
around the data, within which errors are rejected. Also, put into using the idea of support
vector machines (SVMs) [1-4],thefunction fx is made as 8lat as possible in fitting thetraining
data set. We start with the regression function f(x) and it is expressed as f(x) = x , w + b. This
problem can be formulated as an unconstrained minimization problem given as follows:
1
min>?@ w , w + C1, |ξ|D
(1)
(;,<)∈=
2
Where |ξ| ∈ R , (|ξ|D ) = max{0, |A w + b + y | − ε } that denotes the fitting errors and
positive control parameter C here weights the agreement between the fitting errors and the
flatnessof the regression functionf(x). To handle ε-insensitive loss function in the objective
function of the above minimization problem,traditionallyit is reformulated as a constrained
minimization problem expressed as follows:
1
min ∗ w , w + C1, (ξ + ξ∗ )Aw − 1b − y ≤ 1ε + ξ − Aw − 1b + y ≤ 1ε + ξ∗ ξ, ξ∗
(;,<,E,E ) 2
≥ 0.
(2)
This equation (2), which is equivalent to the equation (1), is a convex quadratic minimization
problem with n + 1 free variables, 2m nonnegative variables, and 2m imparity
constraints.However, presenting morevariables (and constraints in the formulation increases
theproblem size and could increase computational complexityfor dealing with the regression
problem.
In our smooth way, we change the model marginally and solve it as an unconstrained
minimization problem preciselyapart from adding any new variable and constraint.

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure1. (a) |x|5D and (b) p5D (x, α) with α = 5,ε=1.

That is, the squares of 2-norm ε- insensitive loss, ‖|Aw − 1b + y|D ‖55 is minimized with weight
J

in place of the 1-norm of ε- insensitive loss as in Eqn (1). Additional, we add the term 5 b5
5
in the objective function to induce strong convexity and to certainty that the problem has a only
global optimal solution. These produce the following unconstrained minimization problem:
1
C
min>?@ (w , w + b5 ) + K|A w + b − y |5D
(;,<)∈=
2
2
L

(3)

This formulation has been projected in active set support vector regression [22] and determined
in its dual form. Motivated by smooth support vector machine for classification (SSVM) [15]
the squares of ε- insensitive loss function in the above formulation can be correctly
approximated by a smooth function which is extremely differentiable and described below.
Thus, we are admitted to use a fast Newton-Armijo algorithm to determine the approximation
problem. Before we make out the smooth approximation function, we exhibit some interesting
observations:
|x|D = max {0, |x| − ε }
= max{0, x − ε } + max{0, −x − ε }
(4)
= (x − ε)( + (−x − ε)( .
In addition, (x − ε)( . (−x − ε)( = 0 for all x ∈ R and ε > 0 . Thus, we have
(5)

|x|5D = (x − ε)5( + (−x − ε)5( .

In SSVM [15], the plus function x( approximated by a smooth p-function,p(x, α) = x +

log(1 + eSQT ), α > 0. It is straightforward to put in place of |x|5D by a very correct smooth
Q
approximation is given by:
5

5

p5D (x, α) = #p(x − ε, α)$ + #p(−x − ε, α)$ .

(6)
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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure1.exemplifies the square of ε- insensitive loss function and its smooth approximation in
the case of α = 5 and ε = 1. We call this approximation p5D -function with smoothing
parameterα. This p5D -function is used hereto put in place of the squares of ε- insensitive loss
function of Eqn. (3) to get our smooth support vector regression(ε-SSVR):
min>?@ ΦD,Q (w, b)
(;,<)∈=

∶=

1
min>?@ #w , w + b5 $
(;,<)∈=
2

C
+ K p5D (A w + b − y , α)
(7)
2
L
1
= min>?@ #w , w + b5 $
(;,<)∈=
2
C , 5
+ 1 pD (A w + b − y, α) ,
2
Where p5D (A w + b − y, α) ∈ R

is expressed by p5D (A w + b − y, α) = p5D (A w + b − y , α).

This problem is a powerfully convex minimization problem without any restriction. It is not
difficult to show that it has a one and only solution. Additionally, the objective function in Eqn.
(7)is extremelydifferentiable, thus we can use a fast Newton-Armijo technique to deal with the
problem.
Before we deal with the problem in Eqn. (7) we have to show that the result of the equation (3)
can be got by analyzing Eqn. (7) with α nearing infinity.
We begin with a simple heading thatlimits the difference betweenthe squares of ε- insensitive
loss function,|x|5D and its smooth approximation p5D (x, α).
Heading 2.2.1.For x ∈ Rand |x| < Z + [:
log 2 5 2σ
p5D (x, α) − |x|5D ≤ 2 \
] +
log 2,
α
α
where p5D x, α is expressed in Eqn. (6).

8

Proof. We allow for three cases. For −ε ≤ x ≤ ε, |x|D = 0 and p(x, α)5 are a continuity
increasing function, so we have
p5D (x, α) – |x|5D = p(x − ε, α)5 + p(−x − ε, α)5
log 2 5
5
≤ 2p(0, α) = 2 \
] ,
α
sincex − ε ≤ 0 and – x − ε ≤ 0.
2cd 5 5

For ε < a < [ + Z , using the result in SSVM[15] that px, α 5 − x( 5 ≤ b Q e +
for |x| < Z , we have
p5D (x, α) – (|x|D )5
= (p(x − ε, α))5 + (p(−x − ε, α))5 − (x − ε)5(

5f
log 2
Q

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013
5

≤ #p(x − ε, α)$ − (x − ε)5( + (p(0, α))5
log 2 5 2σ
≤ 2\
] +
log 2.
α
α
Likewise, for the case of – ε − σ < a < – [ , we have
log 2 5 2σ
] +
log 2.
p5D x, α – |x|D )5 ≤ 2 \
α
α
2cd 5 5
e
Q

Hence, p5D x, α – |x|5D ≤ 2 b

+

5f
log 2.
Q

By Heading 2.1, we have that as the smoothing parameter α reaches infinity, the one and only
solution of Equation (7) reaches, the one and only solution of Equation (3). We shall do this for
a function fD (x) given in Eqn. (9) below that includes the objective function of Eqn. (3) and for
a function g D (x, α) given in Eqn. (10) below which includes the SSVR function of Eqn. (7).
Axiom 2.2.2. Let A ∈ R ⨯ andb ∈ R ⨯ . Explain the real valued functions fD (x) and
g D (x, α) in the n-dimensional real molecular descriptor spaceR :
1
1
5
fD (x) = K"Ag x − b" + ‖x‖55
D
2
2

(9)

L

And
g D (x, α) =

Withε,α > 0.


∑ L
5



p5D ( Ag x − b, α) + 5 ‖x‖55 ,

(10)

1. There exists a one and only solution x of minT∈=> fD (x) and one and only solution xQ of
minT∈=> g D (x, α).
2. For all α > 0 , we have the following inequality:
log 2 5
log 2
‖xQ − x‖55 ≤ m j\
] +ξ
k,
α
α

(11)

Whereξ is expressed as follows:
ξ = max |(Ax − b) |.
l l

(12)

Thus xQ gathers to xas α goes to endlessness with an upper limit given by Eqn. (11).

The proof can be adapted from the results in SSVM [15] and, thus, excluded here. We now
express a Newton-Armijo algorithm for solving the smooth equation (7).
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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

2.2.1A NEWTON-ARMIJO ALGORITHM FOR m-SSVR
By utilizing the results of the preceding section and taking benefitof the twice differentiability
of the objectivefunction in Eqn. (7), we determine a globally and quadratically convergent
Newton-Armijo algorithm for solving Eqn. (7).
Algorithm 2.3.1 Newton-ArmijoAlgorithm For n-SSVR
Start with any choice of initial point (w o , bo ) ∈ R( . Having (w , b ), terminate if the gradient
of the objective function of Eqn. (7) is zero, that is, ∇ΦD,Q (w , b )=0. Else calculate
(w ( ,b ( ) as follows:
1.
Newton Direction:Decide the directiond ∈ R( by allocatingequal to zero the
Linearization of∇ΦD,Q (w, b) all over(w , b ), which results inn + 1
Linear equations with n + 1 variables:
∇5 ΦD,Q (w , b )d = −∇ΦD,Q (w , b ), .
2.

(13)

Armijo Step size [1]: Choose a stepsize λ ∈ R such that:

#w ( , b ( $ = (w , b )+λ d ,

(14)


5r

whereλ = max{1, , , … … }such that:
ΦD,Q #w , b $ − ΦD,Q ((w , b ) +λ d ≥ −δλ ΦD,Q (w , b )d ,

(15)



where δ ∈ b0, 5e .
Note that animportant difference between our smoothingapproach and that of the traditional
SVR [7-9] is that we are solving a linear system of equations (13) here, rather solving a
quadratic program, as is the case with the conventional SVR.

2.3LIBSVM
LIBSVM [18] is a library for support vector machines. LIBSVM is currentlyone of the most
widely used SVM software. This software contains C-support vector classification (C-SVC), vsupport vector classification (v-SVC), ε-support vector regression (ε-SVR), v-support vector
regression (v-SVR). All SVM formulations supported in LIBSVM are quadratic minimization
problems

2.4Potential-Support Vector Machines(P-SVM)
P-SVM [16-17] is a supervised learning method used for classification and regression. As well
as standard Support Vector Machines, it is based on kernels. Kernel Methods approach the
problem by mapping the data into a high dimensional feature space, where each coordinate
corresponds to one feature of the data items, transforming the data into a set of points in a
Euclidean space. In that space, a variety of methods can be used to find relations between the
data.
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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

2.5Experimental Evaluation
In order to evaluate how well each method generalizedto unseen data, we split the entire data
set into two parts,the training set and testing set. The training data was usedto generate the
regression function that is learning fromtraining data; the testing set, which is not involved in
thetraining procedure, was used to evaluate the predictionability of the resulting regression
function.We also used a tabular structure scheme in splitting the entire data set to keep the
“similarity” between training and testing data sets [23]. That is, we tried to make the training
set and the testing set have the similar observation distributions. A smaller testing error
indicates better prediction ability. We performed tenfold cross-validation on each data set [24]
and reported the average testing error in our numerical results. Table 1 gives features of two
descriptor datasets.
Table 1: Features of two descriptor datasets
Data set(Molecular
Descriptors of
Oxazolines and
Oxazoles Derivatives)

Train
Size

Test Size

Full

75 X 254

25 X 254

254

Reduced

75 X 71

25 X 71

71

Attributes

In all experiments, 2-norm relative error was chosen to evaluate the tolerance between the
predicted values and the observations. For an observation vector y and the predicted vector yv ,
the 2-norm relative error (SRE) of two vectors y and yv was defined as follows.
SRE =

‖y − yv‖5
‖y‖5

(16)

In statistics, the mean absolute error is a quantity used to measure how close predictions are to
the eventual outcomes. The mean absolute error (MAE) is given by


1
1
MAE = K|yv − y | = K|e |
(17)
n
n
L

L

As the name suggests, the mean absolute error is an average of the absolute errors e =yv − y ,
where yv is the prediction and y the observed value.

In statistics, the coefficient of determination, denoted R5 and pronounced R squared, is used in
the context of statistical models whose main purpose is the prediction of future outcomes on the
basis of other related information. R5 is most often seen as a number between 0 and 1, used to
describe how well a regression line fits a set of data. A R5 near 1 indicates that a regression
line fits the data well, while a R5 close to 0 indicates a regression line does not fit the data very
well. It is the proportion of variability in a data set that is accounted for by the statistical
model. It provides a measure of how well future outcomes are likely to be predicted by the
model.
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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

R5 = 1 −

∑L(y − yv )5
∑L(y − y)5

(18)

The predictive power of the models developed on the calculated statistical parameters standard
error of prediction (SEP) and relative error of prediction (REP)% as follows:
SEP = z

o.|

∑L(yv − y )5
{
n


100 1
REP(%) =
~ K(yv − y )5 
y n
L

o.|

(19)
(20)

The performancesof models were evaluated in terms of root mean square error (RMSE), which
was defined as below:
∑L#

 =



5

−  $

(21)

Whereyv ,y and y are the predicted, observed and mean activity property, respectively.

3.RESULTS AND DISCUSSION
In this section, we demonstrate the effectiveness of our proposed approachε-SSVR by
comparing it to LIBSVM (ε-SVR) and P-SVM. In the following experiments, training is done
5
with Gaussian kernel function k(x1, x2) = exp b−ϒx − xg  e , where ϒis the is the width
of the Gaussian kernel, i, j = 1, … . . , l. We perform tenfold cross-validation on each dataset and
record the average testing error in our numerical results. The performances of ε-SSVR for
regression depend on the combination of several parameters They are capacity parameter , ε
of ε- insensitive loss function and ϒparameter.  is a regularization parameter that controls the
tradeoff between maximizing the margin and minimizing the training error. In practice the
parameter  is varied through a wide range of values and the optimal performance assessed
using a separate test set. Regularization parameter , whose effect on the RMSE is shown in
Figure 1a for full descriptor datasetandFigure 1b for reduced descriptor dataset.

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure 1a.The selection of the optimal capacity factor (8350) for ε-SSVR(ε=0.1, ϒ=0.0217)
For the Full descriptordataset, The RMSE valuefor ε-SSVRmodel 0.3563 is small for selected
optimal parameter C, compared to RMSE values for other two models i.e. LIBSVM (ε-SVR)
and P-SVM are 0.3665 and 0.5237.Similarly,for the reduced descriptor dataset,The RMSE
value for ε-SSVR model 0.3339 is small for selected optimal parameter C, compared to RMSE
values for other two models i.e. LIBSVM (ε-SVR) and P-SVM are 0.3791 and 0.5237. The
optimal value for ε depends on the type of noise present in the data, which is usually unknown.
Even if enough knowledge of the noise is available to select an optimal value for ε, there is the
practical consideration of the number of resulting support vectors. Ε insensitivity prevents the
entire training set meeting boundary conditions and so allows for the possibility of sparsely in
the dual formulations solution.

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure 1b. The selection of the optimal capacity factor(1000000) for ε-SSVR(ε=0.1, ϒ=0.02)
So, choosing the appropriate value of ε is critical from theory. To find an optimal ε, the root
mean squares error (RMSE) on LOO cross-validation on different ε was calculated. The curves
of RMSE versus the epsilon (ε) is shown in Figure 2a and Figure 2b.

Figure 2a. The selection of the optimal epsilon (0.1) for ε-SSVR( = 1000, ϒ=0.02)
For the Full descriptor dataset , The RMSE value for ε-SSVR model 0.3605 is small for
selected optimal epsilon(ε), compared to RMSE value for LIBSVM(ε-SVR) model is closer i.e.
0.3665 but comparable to the proposed model and bigRMSE value for P-SVM model is
0.5237.
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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure 2b.The selection of the optimal epsilon (0.1) for ε-SSVR(= 10000000, ϒ=0.01)
Similarly , for the Reduced descriptor dataset , The RMSE value for ε-SSVR model 0.3216 is
small for selected optimal epsilon(ε) , compared to RMSE values for other two models i.e.
LIBSVM(ε-SVR) and P-SVM are 0.3386 and 0.4579.

Figure 3a. The selection of the optimal ϒ(0.02) for ε-SSVR(C =1000, ε=0.1)
Parameter tuning was conducted in ε-SSVR, where the ϒ parameter in the Gaussian kernel
function was varied from 0.01 to 0.09 in steps 0.01 to select optimal parameter. The value of ϒ
is updated based on the minimization LOO tuning error rather than directly minimizing the
training error. The curves of RMSE versus the gamma(ϒ) is shown in Figure 3a and Figure 3b.
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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure 3b. The selection of the optimal ϒ(0.01) for ε-SSVR(C =1000000, ε=0.1)
For the Full descriptordataset , The RMSE value for ε-SSVR model 0.3607 is small for
selected optimal parameter ϒ , compared to RMSE values for other two models i.e.
LIBSVM(ε-SVR) and P-SVM are 0.3675 and 0.5224. Similarly , for the Reduced descriptor
dataset , The RMSE value for ε-SSVR model 0.3161 is small for selected optimal parameterϒ,
compared to RMSE values for other two models i.e.LIBSVM(ε-SVR) and P-SVM are 0.3386
and 0.4579.
The statistical parameters calculated for the ε-SSVR, LIBSVM(ε-SVR) and P-SVM models are
represented in Table 2 and Table 3.
Table 2. Performance Comparison between ε-SSVR,ε-SVR and P-SVM for Full descriptor
dataset
Algorithm

(ε, C,ϒ)

Train
Error( )
0.9790
0.9825
0.8248

Test
Error( )
0.8183
0.8122
0.6166

MAE

SRE

SEP

REP(%)

ε-SSVR
ε-SVR
P-SVM

(0.1,1000,0.0217)

0.0994
0.0918
0.2510

0.1071
0.0979
0.3093

0.3679
0.3741
0.5345

53.7758
54.6693
78.1207

ε-SSVR
ε-SVR
P-SVM

(0.1,8350,0.0217)

0.9839
0.9825
0.8248

0.8226
0.8122
0.6166

0.0900
0.0918
0.2510

0.0939
0.0979
0.3093

0.3636
0.3741
0.5345

53.1465
54.6693
78.1207

ε-SSVR
ε-SVR
P-SVM

(0.1,1000,0.02)

0.9778
0.9823
0.8248

0.8181
0.8113
0.6186

0.1019
0.0922
0.2506

0.1100
0.0984
0.3093

0.3681
0.3750
0.5332

53.8052
54.8121
77.9205

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In these tables, statistical parameters R-square (R5 ) ,Mean absolute error (MAE),2-N
Normalization(SRE), standard error of prediction (SEP) and relative error of prediction
(REP%) obtained by applying the ε-SSVR, ε-SVR and P-SVM methods to the test set indicate
a good external predictability of the models.
Table 3. Performance Comparison between ε-SSVR,ε-SVR and P-SVM for Reduced descriptor
dataset
Algorithm

( ε, C,ϒ)

ε-SSVR
ε-SVR
P-SVM

(0.1,1000000,0.02)

ε-SSVR
ε-SVR
P-SVM
ε-SSVR
ε-SVR
P-SVM

(0.1,10000000,0.01)

(0.1,1000000,0.01)

Train
Error( )
0.9841
0.9847
0.8001

Test
Error( )
0.8441
0.7991
0.7053

MAE

SRE

SEP

REP(%)

0.0881
0.0827
0.2612

0.0931
0.0914
0.3304

0.3408
0.3870
0.4687

49.8084
56.5533
68.4937

0.9849
0.9829
0.8002
0.9796
0.9829
0.8002

0.8555
0.8397
0.7069
0.8603
0.8397
0.7069

0.0851
0.0892
0.2611
0.0964
0.0892
0.2611

0.0908
0.0967
0.3303
0.1056
0.0967
0.3303

0.3282
0.3456
0.4673
0.3226
0.3456
0.4673

47.9642
50.5103
68.3036
47.1515
50.5103
68.3036

An experimental results show that experiments carried out from reduced descriptor datasets
shows good results rather than full descriptor dataset. As from can be seen from table 4 , the
results of ε-SSVR models are better than those obtainedby ε-SVR and P-SVM models for
Reduced descriptor data set.

Figure 4. Correlation between observed and predicted values for training set and test set
generated by ε-SSVR
Figure4, 5and 6 are the scatter plot of the three models, which shows a correlation between
observed value and ant tuberculosisactivity prediction in the training and test set.

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International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.2, No.2, April 2013

Figure 5. Correlation between observed and predicted values for training set and test set
generated by ε-SVR

Figure 6. Correlation between observed and predicted values for training set and test set
generated by P-SVM algorithm
Our numerical results have demonstrated that ε-SSVR is a powerful tool for solving
regressionProblems handle the massive data sets without scarifying any prediction accuracy. In
the tuning process of these experiments, we found out that LIBSVM and P-SVM become very
slow when the control parameter  becomes bigger, while ε-SSVR is quite robust to the control
parameter . Although we solved the ε-insensitive regression problem is an unconstrained
minimization problem.
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4CONCLUSION
In the present work, ε-SSVR, which is a smooth unconstrained optimization reformulation of
the traditional quadratic program associated with a ε-insensitive support vector regression.We
have compared the performance of, ε-SSVR, LIBSVM and P-SVM models with two datasets.
The obtained results show that ε-SSVR can be used to derive statistical model with better
qualities and better generalization capabilities than linear regression methods. ΕSSVRalgorithm exhibits the better overall performance and a better predictive ability than the
LIBSVM and P-SVM models. The experimental results indicate ε-SSVR has high precision and
good generalization ability.

ACKNOLDGEMENTS
We gratefully thank to the Department of Computer Science Mangalore University, Mangalore
India for technical support of this research.
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Authors
Doreswamyreceived B.Sc degree in Computer Science andM.Sc Degree
inComputer Science from University of Mysore in 1993 and 1995 respectively.
Ph.Ddegree in Computer Science from Mangalore University in the year 2007.
Aftercompletion of his Post-Graduation Degree, he subsequently joined and served
asLecturer in Computer Science at St.Joseph’s College, Bangalore from 19961999.Then he has
elevated to the position Reader in Computer Science at
Mangalore Universityin year 2003. He was the Chairman of the Department of
Post-Graduate Studies and researchin computer science from 2003-2005 and from 2009-2008 and served
at varies capacitiesin Mangalore University at present he is the Chairman of Board of Studies and
AssociateProfessor in Computer Science of Mangalore University. His areas of Researchinterestsinclude
Data Mining and Knowledge Discovery,ArtificialIntelligence and Expert Systems, Bioinformatics
,Molecular modelling and simulation ,Computational Intelligence ,Nanotechnology, ImageProcessing
and Pattern recognition. He has been granted a Major Research project entitled “Scientific Knowledge
DiscoverySystems(SKDS) for Advanced Engineering Materials Design Applications” fromthe funding
agency University Grant Commission, New Delhi, India. Hehas been published about 30 contributedpeer
reviewed Papers at national/International Journal and Conferences.Hereceived SHIKSHA RATTAN
PURASKAR for his outstanding achievementsin the year 2009 and RASTRIYA VIDYA
SARASWATHI AWARD for outstanding achievement in chosenfield of
activityin the year 2010.
ChanabasayyaM. Vastradreceived B.E. degree and M.Tech.degree in the
year2001 and 2006 respectively. Currently working towards his Ph.D Degree in
Computer Science andTechnology under the guidance of Dr. Doreswamyin the
Department of Post-Graduate Studies and Research in Computer Science,
Mangalore University.

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