The such as Artificial Neural Network (ANN), Fuzzy

The purpose of this study
is to investigate individual employee characteristics and organizational
variables that may lead to employee attrition. In today’s working environment,
a company’s human resources are truly the only sustainable competitive
advantage. Product innovations can be duplicated, but the synergy of a
company’s workforce cannot be replicated. It is for this reason that not only
attracting talented employees but also retaining them is imperative for
success. The study of predicting attrition rate has attempted to explain what
factors make the employees leave and how to prevent the drain of employee
talent. If Attrition Rate can be found to be predictable, the identification of
at-risk employees will allow us to focus on their specific needs or concerns in
order to retain them in the workforce. Two classification methods were used to
develop models for predicting employee attrition rate. Artificial Neural
Network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS).


Keywords: ANN,
ANFIS, Attrition Rate, MATLAB

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Recently, intelligent soft computational techniques such as
Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and (ANFIS) can
model superiority of human knowledge features. They also re-establish the
process without plenty of analysis. Thus these techniques are attracting great
attention in an environment that is obvious with the absence of a simple and
well-defined mathematical model. Besides, these models are characterized by
nonrandom uncertainties which associated with imprecision and elusiveness in
real-time systems. Many researchers have studied the application of neural
networks to overcome most of the problems above outlined.


The fuzzy set theory
is also used to solve uncertainty problems.
The use of neural nets in
applications is very
sparse due to its implicit
knowledge representation,
the prohibitive computational effort and so on. The key benefit of fuzzy
logic is that its
knowledge representation is explicit, using
simple IF-THEM relations. However, it is
at the same time its major limitation. The Attrition Rate Prediction
cannot be easily described
by  artificial
 explicit  knowledge,
 by many
unknown parameters. The integration of neural network into the fuzzy logic system makes it possible to learn from
the prior obtained data sets.

The purposes of this study are to compare the applicability
of ANN and ANFIS in predicting Attrition Rate in an
Organization and to identify
most fitted model
to the study area.





The input data used for Attrition Rate prediction are the different employee characteristics and this
data is acquired by Kaggle, an open
source dataset platform.

This graph presents the correlations between each variables. The size of
the bubbles reveals the significance of the correlation, while the color
present the direction (either positive or negative).








 Artificial neural network (ANN)


A customized
neural network is adopted here. A network first needs
to be trained before interpreting
information. Several different algorithms are available
for training of neural networks, but the back-propagation algorithm is the most versatile
and  robust
 learning  procedure  for multilayer neural networks. Also, the fact that back-propagation algorithms are
especially capable to solve problems of prediction
makes them highly popular.


During training of the network, data are processed through the network until they reach the output layer. In this layer,
the output is compared
to the
measured values. The difference or error between the two is processed back through the network (backward pass) updating the individual weights
of the connections and the biases of the individual
neurons. The input and output data are mostly represented as vectors called training pairs. The process as mentioned
above is repeated for all the training
pairs in the data set, until the network error has
converged to a threshold minimum
defined by a corresponding cost function, usually the root mean squared
error (RMSE).


This customized neural
network is used for predicting Attrition Rate. A number of 15,000 data e.g. were
utilized during training session and 50 data
e.g. were used during testing session. A suitable configuration has to
be chosen for the best performance of the
network. Out of the different configurations
tested, two hidden layer with 50 and 25 hidden neurons
produced the best result. The log sigmoid function was employed as an activation function.
Suitable numbers of epochs have to be assigned to overcome the problem of over fitting
and under fitting of data


ANN structure for
the groundwater

















Neuro Fuzzy Inference System (ANFIS)


ANFIS was originally proposed by JSR Jang. ANFIS is a fuzzy system trained by an algorithm derived from neural network theory. The
algorithm is a
hybrid training algorithm based on back
propagation and the least squares approach.  In
this algorithm, the parameters defining the shape of the membership functions are identified
by a back
propagation algorithm,
while the consequent  parameters 
are identified by the least squares method. An ANFIS can be viewed as a special three- layer feed forward neural network.
The first layer represents input
variables, the hidden layer represents
fuzzy rules, and the third layer is an output


For ANFIS model, similar training and testing data sets were
used as in ANN model. We used Subtractive Clustering
algorithm in ANFIS for training the dataset.





Comparison of ANN and ANFIS models


Results from two models are presented in this section to access and compare the

degree of prediction accuracy and generalization capabilities of the two networks designed in the present problem. The same training and testing data sets were used to
train and test both models to extract more solid conclusions from the comparison results.






Mean square error (MAE), root mean square
error (RMSE) were calculated based on the corresponding measured data. Analysis of data in randomized sets clearly
showed that ANN model is best fit for predicting the Attrition Rate.