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\begin{document}
\title{Predictive Human Resource Analytics using Data Mining Classification 
Techniques}


\author{\IEEEauthorblockN{Zarmina
    Jaffar\IEEEauthorrefmark{1},
    Dr. Waheed Noor\IEEEauthorrefmark{1}, 
    and
    Zartash Kanwal\IEEEauthorrefmark{1}}\\[-0.5em]
\IEEEauthorblockA{\IEEEauthorrefmark{1}
  Computer Science and Information Technology, University of
  Balochistan\\ Quetta, Pakistan.  Email: zaufi\_adii\_89@yahoo.com,
  a.waheed.noor@gmail.com,\\zartash.cs@uob.edu.pk}\\[-0.5em]
}
\maketitle

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\begin{abstract}
The turnover ratio of employees in organization is the most important 
concerns as employee’s switching of organization/job leaves huge gape and 
affects the performance of that organization.Among many, job satisfaction 
is the prime reason of employees to quit/switch, which is also directly 
related to human resource management (HRM) practices of the organization. 
It is always difficult and sometime beyond the control of human resource 
(HR) department to retained their well-trained and skilled employees but 
Data mining can play role to predict those employees who are expected to 
quit/leave an organization such that the HR department can device 
intervention strategy or look for alternative. In this paper, we focus on 
similar problem, where we use data mining techniques such as J48, Naive 
Bayes, and Logistic Regression predict employees who will leave the 
organization. Our data consists of different indicator values and some 
other important features such as number of projects, supervisor evaluation 
score and experience. We show that J48 perform well with accuracy 98.84\% 
and TP rate 0.984\%. Conventional statistical analysis has been used in 
literature to identify important factors affecting employee’s satisfaction 
but there is not agreed set. We also apply data mining techniques to 
identify such factors using two approaches such as Bayesian Network and IR. 
Finally, we provide a decision tree based model for decision makers that 
can easily stimulate employees satisfaction level for better retention 
policy. 
\end{abstract}

\section{Introduction}
Data mining is the method to discover patterns from large amount of data by 
using different procedures. This is used as an analyzer for knowledge 
discovery databases used in decision making process. Large organizations 
employ it chiefly to discover new methods to raise their profits and to 
reduce the cost. Data mining analyze the data and helps to bring up the 
hidden factors so that useful patterns and information can be generated. 
These kinds of findings is definitely help any organization to take future 
decisions in relation to that product.

There are several classification techniques in data mining such as the 
decision tree, neural network, rough set theory, baisian theory and fuzzy, 
see Phyu \cite {phyu2009survey}. Decision takes place between trees, 
popular classification techniques, which make interpretable rules, or 
logic statement, See Jantan et al.  \cite {jantan2010human}. The rules can 
be used to predict the future through the predominantly established method.

Data mining stands out due to its detailed procedures. Various domains such 
as statistics, artificial intelligence, mechanical algorithms, database 
systems, and visualization. Perform these effects on the basis of its 
applications for the business for which human resources management is 
clearly organized. Due to this, data mining has been found in popularity. 
Device with the ability to identify and pass trends within data Knowledge 
with predominantly predictive qualities, See Witten et al. 
\cite{witten2016data}, Kurgan et al. \cite{kurgan2006survey}.

H. Liu et al. \cite{liu1996probabilistic} proposed a consistency based 
facility selection system. This happens when training instances are 
projected on a subset of attributes, then the value of the subset is 
evaluated from the level of stability in the values ​​of the class. 
Consistency of any subset can never be less than the full set of 
attributes; Therefore, general practice is to use this subset evaluator in 
combination with random or excessive search, which seeks the smallest 
subgroup with the same consistency set attributes.

M. Hall \cite{hall1999feature} proposes a new correlation based approach 
to address Correlation based feature selection (CFS) based on dependencies 
in a single dataset, and demonstrates how to apply the classification and 
regression of machines to both issues.

Anirut Suebsing et al. \cite{suebsing2011euclidean}, presented Euclidean 
distance measurements to be used as a KDD dataset selection score. A high-
score attribute greater than the defined threshold, selected as the best 
subset of features.

Zahra Karimi et al. \cite{karimi2013feature}, introduces a hybrid feature 
selection method combining symmetric uncertainty measurement and benefit 
measurement.According to the average fractional value calculated earlier, 
the SU and gain characteristics of each type of correlation feature are 
sorted. A high-level feature was selected within a range. He used the KDD 
dataset and the naïve basic algorithm to evaluate his system.

A. Chaudhary et al. \cite{shashoa2016classification} provides a bayes of 
simple performance and a custom mobile device evaluation on three feature 
selection methods. In this task, the relevant methods, the gain ratio 
method and the information gain method are used.

Ahmad et al. \cite{ahmad2015green} discussed that HRM (Human Resource 
Management is the major coalition of administration which deals with the 
most previous resource of the firm which is human resource. HRM educate 
their employee which can perform different task for the betterment of the 
people, society, surrounding and businesses. GHRM (Green Human Resource 
Management) is also the part of HRM. In an organization the GRM rules
implemented through which their employees were upgraded to improve their 
profession which can help them to accomplish the firm’s ambition in a 
better way.

Savaneviciene et al. \cite{savaneviciene2011human} described that Human 
Resource (HR) is the most valuable resource of any firm. To enhance the 
performance of organization the employees of an organization can follow the 
practices and methodologies of HRM. Most of the organization also select 
the software system which can efficiently control the day to day 
conditioning and necessities of HR department.

Barney et al. \cite {barney1998becoming} conducted that better skill, 
encouragement, refine Human Resource Management carry out organization’s 
constancy and as well as job gratification in which the firm should 
estimate and select to manipulate HR that make their employees stronger 
faith in the firm’s ambitions and valuation.

Jantan et al. \cite {jantan2010applying} described that Human Resource 
Management is responsible for the selection of employees in organization. 
Human Resource professional have to decide the correct employee for the 
task at the correct time at the correct location. 

Arulrajah et al. \cite {arulrajah2016literature} discussed that by using 
Data Mining (DM) classifiers and classification method is very important to 
predict the future behavior of employees through past record sets. 

Arulrajah et al. \cite { arulrajah2015contribution} conducted that Human 
Resource Management can help the organization to demonstrate a well-founded 
administration in the association by upgrading their employees, agreement, 
to accept responsibility, transparency, effectual and efficacy, impartial 
and inclusive and also follow the rules and regulation.

Jiang et al. \cite {jiang2012does} conducted that Human Resource 
Management ordinary serve the honorable persons and also upgrade their 
honorable persons because insufficient recompense salary changeableness can 
also the main reason of the organization’s ethical climate. 

Batt et al. \cite {batt2011employment} In HRM 3 proportion for example 
(skillfulness, encouragement, and upgradation) were appreciative connected 
to humans central and retainer encouragement. Encouragement in Human 
Resource runover, for example, recompense them according to interpretation, 
motivation, benefaction, task protection are another probable to give 
retainer with external encouragement.

Glebbeek et al. \cite {glebbeek2004high} discussed that the observation 
displayed that turnover can have an unfavorable consequence on 
organization’s interpretation. The Human Resource Management should have a 
conception the optimum estimation of the turnover is for their firms.

In this paper, we build data mining model for Human Resource Analytics, 
where we use the employee’s record as our training dataset. The first 
stage, is to train different data mining models from the training data set 
using a suitable validation method. The learned model can be used for 
prediction on future/unseen data in the second stage.

\section{Proposed Methodology}

\subsection{Data Set} 
The primary data and the basic information on Predictive Human Resource 
Analytics collected from Kaggle website, by Vivek Aggrawal (Software 
engineer at Tata Consultancy Servies Gurugram, Haryana, India), for the 
purpose of evaluation by the selected method. In addition, data is 
converted into ARFF (Attribute Relation File Format) for processing in WEKA.

The data used in this proposed study includes 14,999 observations, with 
each row representing one single employee. Fields in the dataset include 
the following 10 variables, See \figurename~\ref{fig:Data_Set_1}.
%
\begin{figure}[h] 
  \centering
  \includegraphics[height=4.5in, width=3.5in]{./images/fig_a2}
  \caption{Flow of proposed model.} 
  \label{fig:Data_Set_1}
\end{figure}
%

The data set prepared ,pre-process and clean using the preprocess tab 
of the explorer window of the WEKA. The pre-processing capabilities of WEKA 
is recapitulate in expansive set of routines which is known as filters. The 
preprocess of a filters basically focus on two kinds of values such as 
instance and attribute in which the data set used to resample the values. 
The instances is divided into two datasets: a training set and a test set. 
70\% of the dataset selected for the training and 30\% for testing and 
correcting the invert  section have been selected to check the ratio of two 
values.

\subsection{Data Pre-processing}
The data which is available for mining is raw data, it is the original data 
Data can be in different formats, it comes from different sources, noise 
data can be irrelevant, data must be preprocessed before applying data 
mining techniques. The data mining algorithm uses the following steps:

\subsubsection{Integration of Data}
If the data comes from many different sources, then the data must have 
different aggregations, including the removal of the incompatibilities 
between datasets of different properties or attribute property values 
between sources of datasets.

\subsubsection{Discretization} 
When the data mining algorithm can not face continuous characteristics, 
then discretization is required to be implemented. In this phase, changing 
a continuous attribute into categorical attribute, only to take some 
discrete values. Discretization often improves the understanding of 
searched knowledge.

\subsubsection{Attribute Selection} 
All features are related, so selecting a subset of the features associated 
with them requires mining all the properties in the selected feature.

\subsection{Feature Selection}
Many unrelated features appear in the data that will be mined. Therefore, 
it needs to be deleted. Many mining algorithms do not perform well when 
they have a large number of attributes or features. Therefore, before you 
apply, select the implementing to select any type of mining algorithm.
The major reason of feature selection is to avoid over-fitting, refine 
model efficiency, and provide faster, more cost-effective products.

The technique we used for feature selection are explained in following 
section, See \figurename~\ref{fig:feature_selection}.
%
\begin{figure}[h] 
  \centering
  \includegraphics[height=2.7in, width=3.2in]{./images/classifi}
  \caption{The components of CFS. Training and Testing data is reduce to 
  contain only the features selected by CFS.} 
  \label{fig:feature_selection}
\end{figure}
%

Choosing the best feature adds an additional complexity to the modeling 
layer, rather than finding the best parameters for the complete set, 
finding the first best feature subset and including the model to customize.

Attribute selection method can be roughly divided into filter and wrappar 
methods. The attribute selection method in the filter view is free to 
select from the data mining algorithm and evaluate the dependencies of the 
features and view only the internal properties of the data. In most cases, 
feature dependencies are calculated and low scoring features are removed.

The observation method becomes very slow compared to the filter method 
because the data mining algorithm assumes that each property is applied to 
a subset. In addition, if many different data mining algorithms apply data, 
the calculation cost of the case method will be higher. Advantages of 
wrapper methods include interaction between model search and feature 
subsets in selection, and remembering feature dependencies. Another feature 
selection technique, called "embedded technology", is proposed, in which 
the best subset of features in the classifier construction process is found 
and can be considered searchable in a subset of facilities and a 
hypothetical combination location. As with the wrapper method, the embedded 
method is specific in a given learning algorithm. The advantage of embedded 
systems is that they involve interaction with the classification model, 
while the wrapper method is used at the same time.

The feature selection techniques include CorrelationAttributeEval which 
requires a combination of the Ranker Search method. The primary focus of 
search behind the use of correlation-based method is to ensure that the 
connection between the variable and components in the testing can be 
estimated, which can be predicted by using the following formula.  
%
\begin{equation*}
  r_{zc}=\frac{kr_{zi}}{k+k(k-1)_{rii}},
\end{equation*}
%
The $r_{zc}$ determines the correlation between the outside variable and 
the summed components where the number of components is represented by k 
and average of the correlation factor between the outside variable and the 
summed components. Lastly, determines the component intercorrelation.

The formulae entail that the enhancement of the correlation factor 
concerning the external variable and the components increases the 
correlation between the outside variable and the composite. This factor is 
true even in the other situation where the correlation factor has been 
lower. This particular feature method used as the supervised classification 
task subset as a heuristic measure. It enables the reduction of the 
redundant attributes. The involvement of a supervised task is done by the 
participation of any feature other than numeric. That is why the attributes 
were further converted into the nominal using the discretization procedure 
in the preprocessing section. 
	
This particular aspect was utilized to enhance the prediction of the 
results by reduction in the redundant attributes and features. However, 
certain techniques and learning algorithms are also required 
that the performance of feature selection has been enhanced, which will be 
discussed in the wrapper section.

\subsubsection{Correlation based Feature Selection}
Feature selection is a preprocessing step to machine learning which is 
effective in reducing dimensionality, removing irrelevant data, increasing 
learning accuracy, and improving result comprehensibility, See Yu et al. 
\cite {yu2003feature} 
%

Steps of Feature Selection:

If the characteristics of a subset are highly correlated with the class, 
but there is not much correlation with other properties of the class, the 
characteristics of the subset are good, See Hall et al. \cite 
{hall1999correlation}

Steps:
\begin{enumerate}
\item  Subset generation: We have used four classifiers to rank all the 
	features of the data set.Then we have used top 3, 4, and 5 features for 
	classification.
\item  Subset evaluation: Each classifier is applied to generated subset.
\item  Stopping criterion: Testing process continues until 5 features of 
the subset are selected.
\item  Result validation: We have used 10-fold cross validation method for 
testing each classifier’s accuracy.
\end{enumerate}


\subsubsection{Information Gain Feature Selection}

Gain ratio is another evaluator indicator used in feature selection 
evaluation, See formulas in equation (1). A higher gain ratio value shows a 
greater correlation between properties relative to class labels.

GainR(Class,Attr) = (H(Class)−H(Class|Attr))/H(Attr) (1)

where H specifies the entropy (information).

Gain ratio AttributeEval method used in Weka as a feature selection method. 
This yield ratio calculates the value of the in gain ratio relative to the 
scale of the class. After 10 fold cross-validation after each property is 
evaluated separately, the "search" method attributes the ranker method 
property to ranker through its separate evaluation.

\subsection{Classification}
Data mining algorithms have three different learning methods: supervised, 
unsupervised, or semi-supervised.

In the supervised learning, the algorithm works with groups of examples 
whose labels are known. In the case of classification work, the label may 
be a nominal value, or in the case of regression, the numeric value can be. 
On the contrary, the examples of labels in the dataset are unknown, and 
algorithms usually have examples of their attribute values. Targeting the 
group according to equality clustering work. Classification tasks can be 
seen as supervised techniques, where each instance corresponds to a class, 
represented by the value of a particular target feature or only a class 
property.
 
The knowledge detected by classification algorithm can be expressed in many 
ways, such as rules, decision tree, bayesian network and so on. The 
different techniques we use for classification are explained in the 
following section, See 
\figurename~\ref{fig:Classification}.
%
\begin{figure}[h] 
  \centering
  \includegraphics[height=2.5in, width=3.2in]{./images/classi_step}
  \caption{Classification step through trainig and testing data.} 
  \label{fig:Classification}
\end{figure}
%

The classification process is divided into two stages: training, when the 
training model is created from the training set, the model is evaluated in 
the test set. At the training stage, all examples of predictive 
characteristics and training sets in the algorithm have access to the 
values ​​of the target attribute, and it uses that information to create a 
classification model. The classification process is divided into two 
stages: training, when the training model is created from the training set, 
the model is evaluated in the test set. One of the key goal of the 
classification algorithm is to maximize the accuracy of the classification 
through the classification model, while predicting the examples in the 
tests planned during the receiving training.

\subsection{Association}
Association is considered to be one of the main technique in data mining 
applications. It reveals all the interesting relationships that might be 
called associations in a large database, See 
\figurename~\ref{fig:association}.
%
\begin{figure}[h] 
  \centering
  \includegraphics[height=1.9in, width=2.7in]{./images/association}
  \caption{Proposed Association model.} 
  \label{fig:association}
\end{figure}
%

\subsubsection{Apriori}
Based on the rules of association to find relations between different 
objects. We first need to find the item set in the data set and analyze 
these item sets. We make association rules and then evaluate the decision. 
According to these rules, the data, finally, select the rules that have 
larger confidence and support than required smallest one (Piatetsky-
Shapiro, 1991).This is usually used in the decision support area. In my 
data set, we can also use association rules. But, the decision is a 
better structure of the tree that we can get the rules more clearly.

\section{Experiment I}
The proposed experiments are capable of decision tree classification 
approaches to distinguish the aptitude design in the subset of Human 
Resource DB and different decision trees and classification approaches are 
used for the given datasets. In this article different classification 
techniques are used for talentd dataset that focus on the accuracy of 
datasets. Different classification approaches depend on the input vaiables. 
To develop the prediction model, the classification techniques were used. 
As we have discussed about decision tree it was also used for prediction 
and machine  learning and neural network etc. There are different types of 
classification techniques such as J48,Nave Bayes, Bayes Net, Logistic, 
and OneR, Jrip, Random Tree, SVM etc. To check the accuracy of 
classification technique we apply 10 folds of cross validation on data set. 
As we have discussed about two types of data sets training sets and testing 
sets. In the Explorer tab we just select the train data set to access the 
classify tab, than load the model which we  have previously save and apply 
different classification techniques to check the accuracy of each 
classifiers. In table I the accuracy for full attributes have been given 
in which J48 will be the best among all the classifiers.

%
\begin{table}[ht]
\caption{Classification results on different classifier using
10 fold cross validation.}
\centering 
\begin{tabular}{c c}   % centered columns (5 columns)
\hline  \hline              %inserts double horizontal lines
Classifier Algorithm & Data Set (\%) \\ [0.5ex]% inserts table %heading
\hline
J48 & 98.39    			\\  % inserting body of the table
Naive Bayes & 93.75     \\
Bayes Net & 84.69       \\
OneR & 89.45            \\
Logistic Regressions & 79.23 \\ [1ex] % [1ex] adds vertical space
\hline						  %inserts single line
\end{tabular}
\label{table:nonlin}			% is used to refer this table in the text
\end{table}
%

\subsubsection{J48}
J48 is one of the best and widely used technique with high accuracy 
generally used as a supervised learning methods. It is basically used for 
prediction and classification techniques and very easy to understand.
%
\begin{table}[ht]
\caption{Classification results on J48 classifier using 10 fold
cross validation.}
\centering 
\begin{tabular}{c c c}   % centered columns (5 columns)
\hline  \hline              %inserts double horizontal lines
Classifier Algorithm & Training Set (\%) & Testing Set (\%) \\ [0.5ex]
\hline
J48 & 98.84 & 97.67			  \\  % inserting body of the table
Navie Bayes & 85.35 & 84.53   \\
Bayes Net & 93.83 & 93.18     \\
OneR & 89.35 & 89.45          \\
Logistic Regressions & 79.75 & 80.21 \\ [1ex] % [1ex] adds vertical space
\hline						  %inserts single line
\end{tabular}
\label{table:nonlin}			% is used to refer this table in the text
\end{table}
%

\subsubsection{Naive Bayes}
Naive Bayes basically based on the theorem of Baye. This is the most 
popular classification technique due to its simplicity, computing 
efficiency and excellent performance for real-world challenges. This train, 
evaluates data very quickly with much greater accuracy.


\subsubsection{Bayes Network}
This is a visual model that can define the set of variable conditional 
independences.

\subsubsection{Logistic}
Logistic regression is the most important modeling tool. It is used to 
predict the analysis and is used in projects because the response  
variables logistic regression is considered as a powerful modeling tool. 
Logistic regression is the correct regression assessment, when structured 
variables are dicotomas (binary). Like any regression analysis, logistic 
regression is a predictive analysis. Logistic regression was considered in 
the character of the reactive variable discrete character. During the 
prediction, it creates a version to expect the likelihood of its occurrence.

\subsubsection{OneR}
Comparing classification techniques with other models is fundamental. OneR 
classifier originally produces a one-degree selection tree expressed as a 
set of guidelines, which everybody examines a special feature. It is very 
simple, reasonably priced technique that produces regularly among the top 
policies with high accuracy for assessing other classification fashion, and 
predictive energy of specific characteristics as a hallmark of the energy.

In these experiments selected classifiers from decision tree algorithm are 
used to check the accuracy of different classification approaches. The 
accuracy for full attributes have been given in table I and accuracy for 
training set and testing set have been in table II. In all experiments J48 
considered to be the suitable algorithm among all the others.
%
\begin{figure}[h] 
  \centering
  \includegraphics[height=1.5in, width=3.5in]{./images/fig_a6}
  %\caption{Classifier algi=rithm.} 
  \label{fig:Experiment_I}
\end{figure}

\section{Conclusion}
We compare different feature selection methods which are nproposed and 
tested on secondary data. Naive Bayes, Logistic Regression, and J48 
classifier are shown in this paper along with the methods of selection of 
various features. Existing feature selection algorithms may not be able to 
generate a valid subset of features for the classification of many 
different regions. Although some algorithms may reduce features, their 
classification accuracy is not high. The proposed information selection 
algorithm based on conditional equivalence produces high efficiency and 
small characteristics in several different data sets, and the 
classification accuracy is higher. Our proposed algorithm will be improved 
by eliminating the relevant and effective functions of the machine

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