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

Introduction

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,

because

it

is

affected

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

the

most fitted model

to the study area.

Data

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

new

information. Several different algorithms are available

for training of neural networks, but the back-propagation algorithm is the most versatile

and robust

technique

for

it

provides

the

most

efficient

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

Figure

3:

ANN structure for

the groundwater

level

model.

Adaptive

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.