Download Research paper: Recruitment Prediction Using ID3 Decision Tree

Scientific Journal of Impact Factor (SJIF): 4.14
e-ISSN (O): 2348-4470
p-ISSN (P): 2348-6406
International Journal of Advance Engineering and Research
Development
Volume 3, Issue 10, October -2016
Recruitment Prediction Using ID3 Decision Tree
Souvik Hazra1, Satyaki Sanyal2
1
2
School of Electrical Engineering, KIIT University
School of Electronics Engineering, KIIT University
Abstract — Recruitment is the process of attracting, selecting and appointing the suitable candidate for jobs within an
organization .It is one of the most important processes in any organization as they look for skilled and qualified
candidates to fill up positions in their organization. The process is done in an efficient way with minimal cost and within
minimal time. The main aim of the paper is to take into account relevant attributes based on quantitative and qualitative
aspects of a candidate such as Years of Experience, Employment Status, Current Salary, Level of Education, Whether
from a top-tier school or not and Internships and design a model accordingly to predict the hiring of a candidate. This
model will help organizations to choose candidates efficiently and within a short period of time.
Keywords- Hiring Prediction; ID3 Algorithm; Decision Tree; Data Mining.
I.
INTRODUCTION
Recruitment process aims at an efficient process to get the right person at right time with minimal cost
and minimal time frame. This paper proposes a system that will predict the chances of getting hired of a candidate based
on different attributes like Years of Experience, Current Salary,Employment Status, Current Salary, Level of Education,
Whether from a top-tier school or not and Internships using ID3 algorithm.
ID3 algorithm
II.
LITERATURE SURVEY
2.1. ID3 Algorithm
Ross Quinlan invented ID3 (Iterative Dichotomiser 3) algorithm in decision tree learning which is used to generate a
decision tree from a dataset. For the classification process using the decision tree technique, a tree is constructed and is
applied to the tuples of the database which results in a classification for that tuple[4]..
The decision tree algorithm is an entropy-based algorithm where the idea is to classify the examples according to their
values of the condition attribute set; the main idea of the archetype is to choose the best attribute for classification from
condition attribute sets[5]. This algorithm is based on Entropy and looks for the best classification attribute from the set.
Generally, the attribute with highest information gain is selected as splitting attribute of the current node. Selection of
splitting attributes is based on the concepts of Entropy, Information Gain, and Gain Ratio.
2.1.1. Entropy
Entropy is used by the ID3 algorithm to find the homogeneity of a sample. Simply, If the sample is fully homogeneous
the entropy will be zero, else one.
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International Journal of Advance Engineering and Research Development (IJAERD)
Volume 3, Issue 10, October -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406
We can also call entropy as the measure in Information Theory which is characterized by the impurity of the sample. If
the target attribute has „c‟ different values, then Entropy S relative to it will be:
Entropy(s) =∑- Pi log2Pi
Where Pi is the probability of S belonging to class „I‟. The reason for Logarithm to be base 2 is because entropy is a
measure of the expected encoding length measured in bits.
2.1.2. Information Gain
The decision tree is constructed in a top-down manner. ID3 takes into account the attribute with the largest gain in
information. The gain here is defined as the amount of information needed after the split is done. The gain is found by
determining the differences of the entropy of original dataset and the weighted sum of the entropies of each subdivided
datasets. The aim is to search for the feature that best splits the target class into possibly the purest of child nodes. This
measure of purity is termed as Information which represents the amount of information required to classify a new
instance of an attribute. The equation for this is:
G(D, S) = H(D) - ∑P(Di)H(Di)
The attribute with the highest or the largest value of Information gain is used as the splitting node.
2.2. CART
The algorithm which is used to construct binary trees is called Classification and Regression Trees or simply, CART
(first introduced by Breiman et al). Here the internal nodes have two outgoing edges. Towing is used to select the splits
and Cost complexity pruning helps to prune the obtained tree. Misclassification costs can be considered with CART in
the tree induction. CART even allows users to equip prior probability distribution.
2.3. C4.5
Another conventional algorithm to spawn decision trees is C4.5. Precisely C4.5 also known as statistical classifier
spawns decision trees which used for classification.
C4.5 selects one component of the data that efficiently divides the data set of N samples into subsets that can either be in
one class or the other, at each node of the tree. The difference in entropy (normalized information gain) results in a
selection of an attribute for data splitting. The attribute factor with the highest entropy makes the decision. The algorithm
then looks for the sub-list with next highest entropy[8].
2.3. CHAID
CHAID stands for CHi-squared Automatic Interaction Detection. It is a type of decision tree technique, based upon
adjusted significance testing (Bonferroni testing).CHAID is capable of prediction, classification and can also detect the
interaction between variables. One of the popular uses of CHAID is with respect to direct marketing to select a set of
customers and predict how their response to some variables influence or affect other variables.
Reasons to Choose ID3

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Training dataset generate comprehensible predictions
Constructs trees rapidly
Builds stubby trees
Discovering Leaf nodes allow test dataset to be pruned decreasing test counts
Trees are created only after searching the whole training dataset
Entire postulate is explored
III. IMPLEMENTATION
Recruitment can be determined as the procedure of searching and select the best-suited applicant for an open position, in
an appropriate and profitable manner. The hiring process involves analyzing the requisite of a position, attracting
applicants to that job, screening and selecting them, appointing, and accommodating the new employee to the company.
Depending on the parameters the human resource managers manually classify whether to take the candidate or not. A
decision tree is implemented to determine the probable outcome of recruitment.
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International Journal of Advance Engineering and Research Development (IJAERD)
Volume 3, Issue 10, October -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406
Fig. 1
The following set of data is considered as the base set for the proposed system. The dataset comprises of different
qualitative and quantitative measures of 13 students. The attributes such as „Years of experience', 'Current Salary', if
„Employed‟, list of „Previous employers‟, highest „Level of education‟, if the candidate belongs from a „Top-tier
institute‟, the number of „Research papers‟ he/she has and if he/she has „Interned‟ in any company(s) has been taken into
consideration.
On the basis of the training set the entropy and the information gain is computed to resolve the splitting aspect for
establishing a decision tree.
The combination of various attributes determines whether the candidate is recruited or not. The quantitative aspects of
„Years of experience‟, 'Current Salary', 'Level of education‟ and „Interned‟ form the major aspects for recruitment
analysis. The qualitative skills like the number of research papers, the number of previous employers helps the managers
to know their experience and sound knowledge in the field. The possible values of each attribute are mentioned below.
TABLE 1
Attributes and their possible values
PARAMETERS
Years of experience
DESCRIPTION
Shows the number of years of
experience of the candidate.
Checks if the candidate is currently
employed.
POSSIBLE VALUES
Numeric ( > 0)
Previous employers
Lists the number of previous
employers.
Numeric ( > 0)
Level of education
Shows the highest level of degree
pursued by the candidate.
{ BS (Bachelors), MS (Masters), PhD
(Post graduate) }
Top tier institute
Checks if the candidate pursued
his/her education from a reputed
institution.
Lists the number of research papers
published by the candidate.
{ Y (Yes), N (No) }
Internships
Shows the number of internships done
by the candidate.
Numeric ( > 0)
Current salary
The salary which the candidate has
been receiving on his/her current job.
Numeric ( > 0)
Employed
Research papers
@IJAERD-2016, All rights Reserved
{ Y (Yes), N (No) }
Numeric ( > 0)
50
International Journal of Advance Engineering and Research Development (IJAERD)
Volume 3, Issue 10, October -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406
A pruned decision tree is formed with leaves as the decision to be hired or not is given by the algorithm. The decision
tree formed accordingly is shown below.
Fig. 2
Shown above is the decision tree where the conditions applied, entropy and the decision taken has been shown. For
example, candidates with salary more than 1275000 are not to be hired and there are 2 such samples who have current
salary more than 1275000.Based on the tree formed one can predict the hiring probability of new candidates by giving
the required attributes as input.
Advantages of Decision Tree[6]:
1.Decision trees utterly perform variable screening or feature selection:
When a training dataset is fitted to the decision tree, the most crucial variables are kept at the top nodes upon which, the
tree is split.
2. Decision trees require comparatively little effort from users for data preparation:
To conquer scale differences in between parameters. Let‟s look at an example. Say a dataset measures revenue generated
in millions and age of loan in years. Now it will require some normalization before being fitted to a regression model.
But a decision tree never requires such transformations because the structure of the tree will remain same with or without
the normalization. Also splitting happens on the basis of the basis of proportion of samples inside the split limits and
hence Decision trees aren‟t sensitive to outliers.
3. Nonlinear relationships between parameters do not influence tree performance:
Extremely nonlinear relationships between variables make models invalid. This is because they result in failing simple
regression model checks. But in case of decision trees, they are independent of presumption of linearity in data. Thus
they can be used in wide range of scenarios.
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International Journal of Advance Engineering and Research Development (IJAERD)
Volume 3, Issue 10, October -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406
4. Easy to interpret and explain to executives:
Indeed, decision trees are uncomplicated, effortless and instinctive to explain.
IV. CONCLUSION
This paper uses ID3 algorithm to generate decision rule which can be used to predict the probability of hiring an
applicant for a job. This helps the recruiter to come into conclusion of hiring an applicant faster and choose the best for
the job. Further implementation can be done for development and application for analysis of similar large datasets.
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