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 on the set of input and output data by an

algorithm derived from the theory of Artificial Neural Networks. 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 three- layer feed forward neural network.

The first layer one represents input

variables, the layer

two represents fuzzy rules, and

the layer 3 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.

Conclusions

In this paper we

showed the ability of ANFIS and the ANN in predicting the Attrition Rate and

potential candidates who are going to leave the firm.

The results showed that the

RMSE, MSE for the training data were 0.088, 0.007 for the ANN model, and 0.160,

0.025 for the ANFIS model. As for unseen data, the RMSE, MSE were 0.8, 0.89 for

the ANN and 0.5, 0.25 for the ANFIS model. The ANFIS model, however, was more

sensitive than the ANN model for the unseen data set and is performing better

for the same.

We can conclude

that ANN model can fit the output better compared to the ANFIS model for the

unseen data set. But ANFIS is better than ANN in generalization and prediction

of unseen data.

.