=Paper=
{{Paper
|id=Vol-2353/paper8
|storemode=property
|title=Application of Global Optimization Methods to Increase the Accuracy of Classification in the Data Mining Tasks
|pdfUrl=https://ceur-ws.org/Vol-2353/paper8.pdf
|volume=Vol-2353
|authors=Anastasiya Doroshenko
|dblpUrl=https://dblp.org/rec/conf/cmis/Doroshenko19
}}
==Application of Global Optimization Methods to Increase the Accuracy of Classification in the Data Mining Tasks==
<pdf width="1500px">https://ceur-ws.org/Vol-2353/paper8.pdf</pdf>
<pre>
  Application of global optimization methods to increase
  the accuracy of classification in the data mining tasks

                            Doroshenko A. V.[0000-0002-7214-5108]

                    Lviv Polytechnic National University, Lviv, Ukraine
                         anastasia.doroshenko@gmail.com



       Abstract. The article describes the solving of data mining task using neural-like
       structures of Successive Geometric Transformations Model (NLS SGTM). The
       main problems of this task are imbalanced dataset and different weigh of errors.
       Therefore, to take into account these features, the method of penalties and re-
       wards was used, as well as a piecewise linear approach to classification. The
       supplement of the methods used by the final optimization procedure is pro-
       posed. The procedure of final optimization using simulated annealing.

       Keywords: data mining, classification, imbalance problem, cost-sensitive
       learning, imbalanced data, principal components, neural-like structure of suc-
       cessive geometric transformations model, NLS SGTM, simulated annealing,
       analysis of the principal components, optimization methods.


  1.     Introduction

In the previous articles [1-2] the methods based on the combination of a Successive
Geometric Transformations Model with the method of penalties and rewards was
described. In addition, was developed the piecewise-linear approach to constructing
separating surfaces in classification tasks.
   The purpose of these methods is to solve the data mining tasks of the classification.
The main features of these tasks are large-size datasets, imbalanced dataset and dif-
ferent weight of errors. The main goal of this research is increasing the accuracy of
classification and minimize the number of penalty points.
   In order to increase the accuracy of classification, we propose to supplement the
developed methods with final optimization procedures, in particular by the method of
random correction of decomposition elements and the method of simulated annealing.


  2.     Problem statement

Today the data mining tasks are widespread because most companies today have a
huge amount of accumulated data with information about sales, customers, orders,
and more. This information is a source of hidden knowledge. In turn, the possession
of this knowledge allows this company to take a leading position in the market, to win
the competitive struggle. Among such tasks, one of the most popular is the task of
classification. These tasks are formulated daily; in such spheres of life how as com-
merce, telecommunication, and chemical industry, target marketing, insurance, medi-
cine, bioinformatics, and others. Researchers use different methods to solve classifica-
tion problems [1, 2, 3, 4].
   The main features of classification tasks in data mining are imbalanced data, dif-
ferent weight of error, huge amounts of data. These features require using some spe-
cial additional methods to well-known methods of classification to provide high accu-
racy of classification.
   Hereby as basic methods of classification, we used the neural-like structure of suc-
cessive geometric transformations model (NLS SGTM) [5, 6]. As an additional
method was used piecewise-linear approach [1] and cost-sensitive learning method [7,
8]. This allowed us to improve the classification accuracy and take into account the
specifics of a specific task. This article proposes to apply global optimization methods
to the neuro-like structure already trained as a result of previous experiments. This
will allow us to find such parameters of the neural-like structure, in which the sum of
points reaches the global max.


   3. Increasing the accuracy of classification using random
    correction of decomposition elements.

3.1.   Analysis of the principal components

The analysis of the principal components is the standard method used to reduce the
dimensionality of data in statistical pattern recognition system and signal processing
systems. However, it is also advisable to use the analysis of the principal components
to solve the problems of data mining because of their high dimensionality [9, 10].
   The main task of statistical recognition is the allocation of attributes - the process
in which the data space is transformed into space of attributes, which theoretically has
the same dimension as the input space. Conversions, however, are usually performed
so that a reduced number of the most effective features can represent the data space.
Consequently, only a substantial part of the information contained in the data remains,
the dimension of the data is reduced. If this approach is applied to data mining task,
we will reduce the size of the input data by extracting non-informative features with-
out losing significant data. Consider a more detailed analysis of the principal compo-
nents (in the theory of information is known as Karhunen-Loeve Transform) [11, 12].
   Assume that there exists a vector X of dimension m, which we want to convey with
the help of l numbers, where l <m. If we simply cut the vector x, this will cause the
mean square error to be equal to the sum of the dispersions of the elements carved out
of the vector x. It is necessary to find some linear transformation T, for which the
value of the mean square error for the reduction of the vector X will be optimal. In
this case, the transformation T must have the property of a small dispersion for its
individual components. The analysis of the principal components maximizes the rate
of dispersion reduction and, accordingly, the probability of the correct choice [13,
14].
   Let X be an m-dimensional random vector from the initial set of data. The mean
value of this vector equal zero.

                                       E(X)=0,                                            (1)

where E is the operator of statistical expectation. If X have a nonzero average, then
you can calculate this value before the analysis begins. Let q be a unit vector with
dimension m, the vector X is projected on vector q. This projection is defined as the
product of the vectors X and q:
                                    A=XTq=qTX                                         (2)
with restriction
                                 ||q||=(qTq)0.5 = 1.                                  (3)
   Projection A is a random variable that has an average value and variance that is re-
lated to random vector statistics X. Dispersion A equals
                  σ2=E[A2]=E[(qTX) (XTq)]= qTE[(XXT]q = qTRq                          (4)
   The matrix R of dimension mm is the matrix of the correlation of the random vec-
tor X defined as the expectation of the product of the random vector X on itself:
                                           R=E[XXT]                                   (5)
   The matrix R is symmetric, then:
                                               RT=R.                                  (6)
   It follows from (6) that if a and b are arbitrary vectors with dimension m1 then:
                                           aTRb=bTRa                                  (7)
   From equation (4) it follows that the projection A is a function of an odd vector q.
So, we have:
                                           (q) =σ2= qTRq,                            (8)
(q) – dispersion probe.
   Directly the principal components are defined as follows. Let the data vector x be a
realization of the random vector X. Since there are m possible values of the single
vector q, then we must consider m possible projections of the data vector x. By for-
mula (2)
                                 aj = qjTx=xTqj, j = 1,2,…,m                          (9)
where aj - the projections of the vector x on the main guides, represented by single
vectors qj. These projections are called principal components, their number corre-
sponds to the dimension of the data vector x. In this case formula (9) can be consid-
ered as the analysis procedure.
                     Vector of             Encoding      Vector of the main
                     input data              device         components


                                                       

 Fig. 1. Scheme of the procedure for analysis of the principal components. The encoding step
   Allocation of the principal components (PC) is also proceeding on the outputs of
the hidden layer of the neural-like structure of GTM. After selecting the PC, we create
and teach an additional neural-like structure (Fig. 2), where the inputs are the vectors
of the PC, and the outputs are the significance of the corresponding outputs of the
initial training data [15, 16, 17].




         Fig. 2. The structure of neural network for obtaining decomposition elements

  The feature of this neural network is the representation of the output of the synapse

          z well as weak correlation z . In the mode of application on the inputs
            m
as y                                            i
            i 1 i
of the trained neural network, input vectors are:
                                      0 0 ...0  y0
                                       1 0 ...0  y1
                                       0 1 ...0  y2
                                       ..................
                                       0 0 ...1  ym
  Define  0  y0 ,  i  yi  y0 , i  1,2,..., m.
  Get the elements of decomposition z0  y0 , zi  PCi  i , i  1,2,..., m.
  Initially we have
                               y  i0 zi or y  i0 zi ki ,
                                        m                   m
                                                                                        (10)

where zi  the elements of decomposition, k i =1, i = 1,2,…, m .
   For classification tasks, appropriate indicators are optimized (percentage of prop-
erly classified specimens, number of penalty points, average arithmetic or mean
square error) by random correction of elements of decomposition of coefficients k i .
Random correction of coefficients k i can be performed simultaneously for all com-
ponents or for each component independently. It should be noted that component-
based random correction is appropriate for prediction since it has been experimentally
confirmed that the components are practically independent [18, 19].
  Consider a more detailed algorithm for random correction.
3.2.   Increasing the accuracy of the classification tasks based on correction of
       decomposition elements by random correction

   Let us consider the case of optimizing the accuracy of the classification problem
solving for a case where the recognized data belong to one of the two classes.
   Re-coding the input file: outputs of the elements belonging to class 1, assign 1, the
outputs of the elements belonging to class 2, assign -1.
   The problem of recognition is solved as a prediction problem by the formula (3),
but after obtaining a value, we analyze it by the formula (11):
                                   class1, y  0,
                                 y                                                (11)
                                   class 2, y  0.
   For the task of recognizing the criteria by which optimization can be carried out,
there may be the number of penalty points, the percentage of incorrectly classified
representatives of class 1, the percentage of incorrectly classified representatives of
class 2.
   Then the percentage of incorrectly classified representatives of classes is calculated
as follows:
                  ErrC1 =100% NWC1/NC1, ErrC2 =100% NWC2/NC2                         (12)
where NC1 is the number of representatives in class 1 in the training sample, NC2 –
number of representatives of class 2 in the training sample, NWC1 – number of incor-
rectly classified representatives of class 1, NWC2 – number of incorrectly classified
representatives of class 2.
   In order to calculate the number of penalty points, it is necessary to determine the
fines that count for erroneous recognition. So if P1 is a fine charged if the element of
class 1 is recognized as an element of class 2 and P2 is a fine charged if a class 2
element is recognized as an element of class 1.
   Then the value of the penalty function - the total number of received penalty points
(PP) is:
                                 PP=ErrC1P1+ErrC2P2.                                 (13)
   Optimization method for classification tasks based on correction of decomposition
elements by random correction:
    1. Initial values ki=1.
    2. We calculate the value of the error, which is optimization (PP, ErrC1 or
ErrC2).
    3. Using a random number generator with a uniform distribution, we choose the
value ΔD from the range (–D, D).
    4. Calculate new values k i = k i + ΔD.
   5. Calculate the value of outputs with new k i .
   6. Convert the resulting value to the designation of the class to which this element
belongs.
   7. Calculate the new value of the error, which is optimization (PP, ErrC1 or
ErrC2).
   8. Compare the value of the calculated error with the pre-calculated value. If the
new value is less than the previous one, we will remember it as well as the current
coefficients k i . Go to step 3. Otherwise, go to step 3 without remembering.
    9. Continue optimization until the predefined desired optimization value is
reached, or until the time t has expired.
   After performing the optimization method by randomly correcting the decomposi-
tion elements, the resulting coefficients are used for further classification.


   4. Application of global optimization methods to increase the
    accuracy of classification in the tasks of the data mining

4.1.   Method of simulated annealing
   Annealing method is an algorithmic analogue of the controlled cooling process. It
was proposed in 1953 by N. Metropolis and refined by numerous followers. Today it
is considered one of the few methods by which one can practically find the global
minimum of functions of several variables. Consider a more detailed method of simu-
lated annealing [20, 21].


Algorithm of simulated annealing method:
1. Start the process from the starting point at a given initial temperature T=Tmax.
2. As long as T>0, repeat L times the following actions:
      • choose a new solution w' from the vicinity w;
      • calculate the change of target function; Δ = E(w')- E(w);
      • if Δ≤0, take w = w'; otherwise, if Δ>0, take w = w' with probability exp(-Δ/T)
        by generating a random number R from the interval (0,1), then comparing it
        with the value exp(-Δ/T); if exp(-Δ/T) > R, take a new solution w = w'; in other
        case - ignore it.
3. Reduce the temperature ( T  rT ) using the reduction coefficient r, selected from
the interval (0,1) and return to step 2.
4. After lowering the temperature to zero, apply one of the deterministic methods
(Levenberg-Marquardt algorithm, error-return algorithm, fastest-speed algorithm,
etc.) to achieve the minimum of the target function.
   The concept of "temperature" in this algorithm is quite formal, since the presented
optimization model is only a mathematical analogy of the annealing process.
   The efficiency of the annealing algorithm has an extremely high impact with the
choice of parameters such as the initial temperature Tmax, the coefficient of reduction
of temperature r and the number of cycles L, performed at each temperature level.
   The main problem is to determine the threshold level optimal for each annealing
simulation process. For some practical tasks, this level may have different meanings,
but the overall range remains unchanged. As a rule, the initial temperature is selected
so as to ensure the implementation of about 50% of the subsequent random changes in
the solution. Therefore, knowledge of the pre-distribution of such changes makes it
possible to estimate the initial temperature approximately.
   Numerous computer experiments [22] prove that in the case where the time limit is
small, the best results give a single implementation. If simulation can be long lasting,
then statistically better results can be achieved thanks to the multiple implementation
of the annealing simulation if the value of the coefficient r is close to 1.
   If we compare genetic algorithms with an annealing algorithm, then, in spite of the
significant external difference between the algorithms, they are essentially similar in
nature. An annealing algorithm according to [23] can be considered a genetic algo-
rithm with a population consisting of one instance. Consequently, an algorithm for
simulating annealing of a metal can be regarded as an algorithm that has only a muta-
tion operation, but not cross-linking.
   In addition, if we compare these two algorithms from the applied point of view,
then it should be noted that, according to Kohonen's study [7], in the case when the
initial solution is sufficiently close to optimal, the annealing algorithm of the metal
has significant advantages over the genetic algorithms from a computational point of
view.
   Since in our study the initial data are pre-processed by methods of fines and incen-
tives with sampling alignment and piecewise linear classification on the basis of the
model of geometric transformations, then the initial solution of the problem is suffi-
ciently close to the optimal. Accordingly, in this case, it is more appropriate to choose
for optimization of the solution.


Improvement of accuracy of the decision of tasks of intellectual data analysis on
the basis of correction of elements of decomposition by algorithm of simulated
annealing
   Let's consider a more detailed algorithm of simulated annealing of metal in combi-
nation with methods of fines and incentives and piecewise linear classification on the
basis of a model of geometric transformations.
   Fig. 3 depicts the structural scheme of the developed neural network based on the
model of geometric transformations, where x1 , x2 ,..., xn primary features of classifica-
tion objects – input data, PC1, PC2, ..., PCn  the principal components derived from input
data, w1 , w2 ,..., wn  weight coefficients, y  an output that indicate on belonging to
certain classes.
                     Fig. 3. The scheme of neural-like structure GTM

        Functioning of such a neural network can be described by the formula 14.
                                       y  i 1 PCwi
                                               n
                                                                                  (14)
           The method of simulating annealing of a metal is proposed to be used to op-
timize weight coefficients so that the resulting amount of penalty points is minimal,
that is, the optimization parameter is the amount of penalty points [15, 17].




                Fig. 4. The division tree into classes based on NLS SGTM

   As can be seen from the flowchart describing the solution of the data mining prob-
lem by combining the method of fines and incentives, simulated annealing and piece-
wise linear approach, a modified annealing method for which the target function is the
number of penalty points, will be applied separately for each cluster [22, 24].
   Accordingly, if we have a two-step division into clusters for a sample of n classes,
then we will have division into clusters, which is depicted in Fig. 4, and for each of
the clusters a modified annealing algorithm will be implemented.
  5.     Experimental results

This article describes the solving of classification task, which was formulated in [1].
The training sample describes the transactions carried out by credit card holders
within two days and consists of 284,807 lines and 31 columns Also, the dataset con-
tains one target feature 'Class', which shows the client's affiliation to one of two
classes - frauds or ordinary clients. The main feature of the dataset is that the data set
is highly unbalanced - only 492 transactions out of 284807 (0.172% of all transac-
tions) have the value of the target field 1, that is, customers are fraudulent. The data-
set has been collected and analyzed during a research collaboration of Worldline and
the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de
Bruxelles) on big data mining and fraud detection [7].
   According to the subject area, a matrix was formed. By analyzing this matrix, it
can be seen that a properly classified vector that belongs to the "fraud" class has a
much greater weight than a properly classified "ordinary client" vector. At the same
time, the case where an ordinary customer is classified as fraud has the highest num-
ber of penalty points (Table 1).
   Then we used the modified method of imitation of annealing of the metal with pa-
rameters: initial temperature T=Tmax=20895, L=100, R is a random number from the
interval (0,1), r=0,9.

                   Table 1. Matrix of penalties and rewards solving task

 Matrix of Rewards                     Values of Rewards and Penalties
   and Penalties
                                    The vector is                       The vector is
                                recognized as class 1               recognized as class 2
 The vector belongs
                                           1                                 -3
     to class 1
  The vector belongs
                                          -2                                 5
      to class 2


    After lowering the temperature to zero, apply one of the deterministic methods
(Levenberg-Marquardt algorithm, error-return algorithm, fastest-speed algorithm,
etc.) to achieve the minimum of the target function. The results of classification are
described (Fig 5, Fig.6).
Fig. 5. Results of classification using NLS SGTM with the method of penalties and rewards
and the method of simulated annealing (in vectors)




Fig. 6. Results of classification using NLS SGTM with the method of penalties and rewards
and the method of simulated annealing (in points)


  6.     Conclusion

The application of global optimization methods to classification in the data mining
tasks allowed to increase the accuracy of classification, especially in combination
with other methods of classification, such as neural-like structure of successive geo-
metric transformations model. Also, the method of simulated annealing successfully
combined with such methods as piecewise-linear approach and cost-sensitive learning
method. The application of the method of simulated annealing made it possible to
reach the point of the global maximum and minimize the amount of penalty points for
this task.
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