Discipline Decision Tree Classification Algorithm and Application
based on Weighted Information Gain Ratio
Yan Xia
1
, Jian Shu
2
, Na Xu
3
and Hui Feng
1
1
Shanghai Joint Laboratory for Discipline Evaluation, Shanghai Education Evaluation Institute, Shanghai, China
2
Shanghai General Motor, Shanghai, China
3
Shanghai Municipal Education Examinations, Shanghai, China
Keywords: Data Mining, Information Entropy, Information Gain Ratio, Decision Tree, Discipline Classification,
Discipline Evaluation.
Abstract: Discipline evaluation is an important part in higher education evaluation. It plays a significant role in
discipline construction in universities and colleges. It is challenging how to use scientific discipline
evaluation to classify disciplines, such as advantageous disciplines and newly-emerging ones. This paper
proposes an algorithm of discipline decision tree classification based on weighted information gain ratio. It
determines evaluation attributes and creates decision tree according to weighted information gain ratio.
Discipline classification rules are deduced by decision tree. An automatic classification system is developed,
implementing the algorithm and analysing data from universities and colleges in Shanghai. Experimental
results show that our scheme can achieve about 83.33% accuracy in forecasts. It provides advice and
guidance for discipline evaluation, and establishes foundation for discipline development strategy.
1 INTRODUCTION
Discipline is a basic unit of universities and colleges.
Discipline construction is the core of constructions
in universities and colleges, to improve talent
cultivation quality and scientific research level, and
to serve society. At present, most universities and
colleges in China have completed the layout
adjustment and structure scale of discipline. They
come into a new stage of improving discipline
construction quality, cultivating discipline
characteristics, forming discipline advantages, and
promoting discipline development (Han and Mei,
2011). At the stage, it is significant to develop
advantageous disciplines and newly-emerging
disciplines. Therefore, it is important how to use
scientific method to carry out discipline evaluation,
to classify disciplines to select advantageous
disciplines and newly-emerging ones. Now
discipline evaluation is usually carried out in a way
which combines an objective calculation of data and
peer review. The discipline evaluation indicator
system mainly focuses on a university or college’s
teaching staff and resources, its scientific research
level, its talent cultivation quality and its academic
reputation. The data come from officially released
information for public use and the materials
submitted by the universities and colleges for
evaluation. Data mining is usually used to determine
advantageous disciplines and newly-emerging ones.
The widely used tool for data mining analysis in
academic research and evaluation is bibliometric,
which evaluates discipline according to indicators
related to articles. Although it is objective and easy
to operative, it is mainly focusing on scientific
research level, and neglecting other perspectives. It
is difficult to implement scientific and
comprehensive discipline evaluation only from the
perspective of bibliometric (Hood and Wilson,
2001). It has become a hotspot in higher education
field to establish a discipline evaluation system
based on objective data in order to make a scientific
classification of disciplines in universities and
colleges. With its help, the educational
administrative department can easily understand the
current situation of discipline development, and can
promote the development of higher education in
China healthily and rapidly.
This paper proposes a discipline decision tree
classification algorithm based on weighted
information gain ratio. It determines evaluation
attributes and establishes decision tree according to
Xia, Y., Shu, J., Xu, N. and Feng, H.
Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio.
In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016) - Volume 2, pages 77-84
ISBN: 978-989-758-179-3
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
77

different weighted information gain ratio. Discipline
classification rules are deduced by decision tree. An
automatic classification system is implemented. It
investigates the application of data mining
technology in discipline classification, and provides
advice for the discipline construction in universities
and colleges.
2 RELATED WORK
Advantageous disciplines and newly-emerging
disciplines are the basis of development of first-class
discipline in the world. They play important roles in
the development of the discipline cluster. At the
same time, they take advantage of complementary
disciplines to promote cooperative development
among related disciplines. At present, discipline
evaluation is commonly used to determine whether it
is advantageous discipline, newly-emerging one or
not. The research of discipline evaluation is
composed of several categories. One kind of
discipline evaluation is based on university ranking,
such as Times Higher Education World University
Rankings (THE) (Marijk, 2008), U.S. News College
Rankings (USNWR) (Jamil and Alenoush, 2007),
China Discipline Ranking (CDR) from China
Academic Degrees and Graduate Education
Development Center (CDGDC, 2013). One kind of
discipline evaluation is based on scientific mapping,
such as Bibliometric Rankings from the Centre for
Science and Technology Studies (CWTS) in Laiden
University of Holland (Moed, 2006). One kind of
discipline evaluation is based on tendency research,
such as discipline value evaluation in The National
Center for Scientific Research (CNRS) in France.
Another kind of discipline evaluation is based on
scientific fund management, such as evaluation for
the financing disciplines from Biotechnology and
Biological Sciences Research Council (BBSRC)
(Aghion et al., 2010). All the above discipline
evaluation uses the traditional method, which is
combined of subjective and objective evaluation,
such as expert assessment and bibliometric. The
evaluation process is complex. The evaluation result
is easy to be influenced by the subjectivity and so
on.
In order to solve the existing problems in
discipline evaluation, this paper proposes a new
discipline decision tree classification algorithm
based on weighted information gain ratio. An
automatic discipline classification system is
implemented, verifying the algorithm and analyzing
data from universities in Shanghai. It provides
advice and guidance for comprehensive discipline
evaluation and developing strategy of disciplines.
3 DISCIPLINE DECISION TREE
CLASSIFICATION
ALGORITHM
The discipline decision tree classification algorithm
determines evaluation attributes according to
weighted information gain ratio and correlation
between them. Then it establishes decision tree. The
decision tree is a directed graph to classify items. It
consists of a root node (a node in the graph to which
no other node points), internal nodes (nodes that are
pointed at and to other nodes), and leaves (nodes
that don’t point to other nodes) (Han et al., 2011).
The classified item travels from the root to one of
the leaves, where classification is made. Discipline
classification rules can be deduced by decision tree.
3.1 Basic Definitions
Definition 1 Let S be the set of training samples, and
C
i
the set of all classification attributes. Let S
Ci
be
the subset of S. Probability mass function P
i
is
defined as (1). T(S) is the cardinal number of S.
P
i
= T(S
Ci
) / T(S) (1)
Definition 2 The entropy of S relative to C
i
is
defined as (2). Accum is accumulation of P
i,
i=1…m.
I(S) = - accum (P
i
*Log
2
P
i
) (2)
Definition 3 Let D be the subset of S, which
contains several different evaluation attributes, { A
1,
A
2, …
A
n
}. If each A
i
has k
i
corresponding
characteristics, D can be divided into k
i
subsets
according to evaluation attribute A
i
. The entropy of
D relative to A
i
is defined as (3).
E(D, A
i
) = - accum{ [T(D
ij
) /accum T(D
ij
) ]
*I(D
i
j
)}
(3)
Definition 4 The information entropy of D relative
to evaluation attribute A
i
is defined as (4).
G(D, A
i
)= I(D) - E(D, A
i
) (4)
Definition 5 The information entropy of evaluation
attribute A
i
is defined as (5).
SInfo(D, A
i
)= -accum{ [T(D
ij
) /accum
T(D
i
j
) ] *Log
2
[T(D
i
j
) /accum T(D
i
j
) ] }
(5)
Definition 6 Considering that the relationship
between evaluation attributes has effect on
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classification prediction, the information entropy of
evaluation attribute A
i
will be adjusted. If A
i
has m
i
non-associated characteristics, the mean value of
non-associated information entropy of evaluation
attribute Ai is defined as (6).
SInfo(D, A
i
)’= -accum{ [T(D
ij
) /accum
T(D
ij
) ] *Log
2
[T(D
ij
) /accum T(D
ij
) ] } / m
i
(6)
Definition 7 The information gain ratio of evaluation
attribute Ai is defined as (7).
GRatio(D, A
i
)= r * G(D, A
i
) / [SInfo(D, A
i
)
+t* SInfo(D, A
i
)’]
(7)
The r is set as the weight of evaluation attribute
Ai. GRatio increases as far as r increases. So the
corresponding evaluation attribute is more probably
used as partition. On condition that there is same
number of items in the evaluation attributes, SInfo
increases while GRatio decreases if a special
evaluation attribute has more classification
attributes. Thus the corresponding evaluation
attribute is less probably used as partition. t is set as
the correlation coefficient, and t belongs to [0,1].
The value of t can be adjusted in the process of
algorithm optimization.
3.2 Workflow
The work flow of Discipline Decision Tree
Classification Algorithm is shown as algorithm 1
according to the above definition.
Algorithm 1: DTCA-WIGR (Discipline Decision
Tree Classification Algorithm based on Weighted
Information Gain Ratio)
Input: Samples (the set of training samples),
Attributes (the set of evaluation attributes)
Output: The decision tree of discipline
classification
Work flow:
DTCA-WIGR (Samples, Attributes)
Begin
Create root node R-Node;
If (All items in Samples belong to
same classification attribute C) then
Return R-Node as leaf. Mark it as C;
If (There are no extended evaluation
attributes in Attribute) then
Return R-Node as leaf. Mark it as
classification attribute, which most
items in Samples belongs to;
Else // R-Node is non-leaf.
Continue to classify them.
a. Calculate the information gain
ratio GRatio for each evaluation
attribute A
i
;
b. Select the evaluation attribute
Amax, which has the maximum GRatio;
c. Mark R-Node as evaluation
attribute A
max
;
d. Samples are divided into k
max
subsets, according to the number of
characteristics in A
max
;
e. For each ( S
i
)
e.1. A branch with condition k
i
is
generated according to R-Node;
e.2. If (There are no items in Si)
then
Return Si as leaf. Mark it as
classification attribute, which most
items in Samples belongs to;
e.3. Else
DTCA – WIGR (Si, Attributes - Amax),
End
The recursive step of algorithm DTCA-WIGR
stops when it meets with following conditions.
1. All items in Samples belong to same
classification attribute.
2. There are no extended evaluation attributes in
Attribute for partition.
3. There are no items in S
i
.
The time complexity of algorithm DTCA-WIGR
is O[cardinal(Attributes) * cardinal (Samples) *
Log2 cardinal (Attributes)]. cardinal (Attributes) is
the cardinal number of the set of evaluation
attributes. cardinal (Samples) is the cardinal number
of the set of training samples.
Noise and outlier appear in the process of
constructing decision tree. Algorithms of pruning
decision tree can be used to improve accuracy of
classification (Quinlan, 1987).
4 APPLICATION OF THE
ALGORITHM
4.1 Discipline Evaluation Indicator
System
This paper uses evaluation indicators from
Discipline Evaluation Indicator System in 2012,
which is promulgated by China Academic Degrees
and Graduate Education Development Center
(CDGDC, 2012). The content of Discipline
Evaluation Indicator System is shown in Table 1.
The Discipline Evaluation Indicator System is
composed of 4 primary indicators, including
Teaching Staff and Resources, Scientific Research
Level, Talent Cultivation Quality, and Academic
Reputation. Each primary indicator is composed of
Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio
79

several secondary indexes, 17 secondary indicators
in all. Each secondary indicator contains a number
of observation points with different weight, which
can be considered as tertiary indicators. For
example, Teaching Staff and Resources, one of the
primary indicators, contains 4 secondary indicators.
There are 12 observation points in expert team, one
of the secondary indicators, such as the number of
academicians in China Academy of Sciences or
China Academy of Engineering, the number of
distinguished experts in “Thousand Talents Plan”,
the number of Yangtze River Scholar and so on.
Table 1: Discipline Evaluation Indicator System
promulgated by CDGDC.
Primary Secondary
A.TeachingStaff
andResources
A1. Expert Team
A2. The ratio of Students to
Teachers
A3. Full-time Teacher Staff
A4. Outstanding Disciplines and
Laboratory
B.Scientific
ResearchLevel
B1. Representative Academic
Papers (including domestic and
foreign, quality and quantity)
B2. Scientific Research Award
B3. Academic Publications and
Patent transformations
B4. Representative Research
Projects
B5. Creativity of Art (only for
disciplines of arts)
B6. Architectural Design (only for
disciplines of architecture)
C.Talent
Cultivation
Quality
C1. Quality of Teaching and
Teaching Materials
C2. Quality of Dissertation
C3. International Exchange
C4. Sports Competitions (only for
disciplines of sports)
C5. Outstanding Students and
Graduates
C6. Number of Granting Degrees
D.Academic
Reputation
D1. Discipline Reputation
(including academic reputation,
social contribution, and academic
ethics, etc)
The weight of the primary indicator is r
i
. The i
belongs to [A, B, C, D]. The weight of the secondary
indicator is r
j.
The j belongs to [1, 2, ..., m]. The m is
the cardinal number of corresponding secondary
indicator. The weight of the tertiary indicator is r
k.
,
The j belongs to [1,2,...,n].. n is the cardinal number
of corresponding tertiary indicator. The records in
database map to the tertiary indicator. The weight of
the evaluation attribute r
ijk
is defined as r
ijk
=r
i *
r
j *
r
k.
This paper analyses the relationship between
evaluation attributes by principal component
analysis (Yang and Feng, 2012). Non-associated
characteristics of evaluation attributes are recorded
in database to calculate the mean value of non-
associated information entropy, which helps to
measure information gain ratio of evaluation
attribute
4.2 Data Selection
To ensure authenticity, reliability and authority, the
related data come from officially released
information for public use and the materials
submitted by the universities and colleges for
evaluation, both of which are subject to vigorous
verification. They are all focusing on tertiary
indicators. The data are integrated into the discipline
basic information table in the database. The table
structure is shown in Table 2, which defines 76
evaluation attributes together with some primary key
and foreign keys. The table of weight is set to keep
the weight of each evaluation attributes. The table of
relationship is set to keep the associated
characteristics.
Table 2: Table structure of discipline basic information.
N Meaning Name Type …
1
University or
College ID
UCID char …
2
University or
College Name
UCName varchar …
3 Discipline ID DID char …
4 Discipline Name DName varchar …
5
Number of
Academicians
Academic
ians
smallint …
6
Number of
Experts in
“Thousand
Talents Plan”
ExpertsTT
P
smallint …
7
Number of
Distinguished
Young Scholars
for The National
Science Fund
YScholar
TNF
smallint …
8
The ratio of
Doctors to
Teachers
RatioDT float …
9
The ratio of
Masters to
Teachers
RatioMT float …
10
Number of
National
Outstanding
Disciplines
NODiscip
lines
smallint …
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80

Table 2: Table structure of discipline basic information
(Cont.).
N Meaning Name Type …
11
Number of Highly
Cited Papers by
ESI
HCPapers
ESI
medium
int
…
12
Number of papers
in
SSCI,AHCI&CSS
CI,CSCD
PapersSA
CC
medium
int
…
13
Number of
Natural Science
Award
NaturalSA
medium
int
…
14
Number of
Academic
Publications
APublicati
ons
medium
int
…
15
Number of Patent
Transformations
PatentTSF
medium
int
…
16
Number of
National Natural
Science Fund
NationalN
SF
medium
int
…
17
Number of
Projects from
Provinces and
Ministry
ProjectsP
M
medium
int
…
18
Number of
National Teaching
Achievement
Award
NTeachin
gAA
medium
int
…
19
Number of
Projects supported
by National
Science eand
Technology
Ministry
NationalS
TM
medium
int
…
20
Number of
Outstanding
Graduates
Outstandi
ngGD
medium
int
…
… … … … …
77
Passing Rate of
Doctoral
Dissertation
PRateDD float …
78
Number of
Foreign Students
ForeignST
medium
int
…
79
Number of
Exchange
Students
Exchange
ST
medium
int
…
80
Number of
Granting Degrees
GrantingD
egrees
medium
int
…
81 Year Year
medium
int
…
82
Classification
Type
CType varchar …
We shall do some preprocessing works, such as
data cleaning, data integration, data transformation,
data reduction and so on since data from source
databases are incomplete, inconsistent, and
redundant (Carlo, 2010).
4.3 Automatic Discipline Classification
System
4.3.1 System Structure
The system structure of automatic discipline
classification system based on weighted gain ratio is
shown in Figure 1. The process is as follows.
Create model: Train from sample dataset by
Discipline Decision Tree Classification Algorithm.
Create decision tree, and deduce classification rules.
Optimize model: Evaluate and analyse existing
rules by testing dataset. Optimize classification
rules.
Apply model: Apply the optimized classification
rules on new data to classify disciplines.
Figure 1: System structure of automatic discipline
classification system.
4.3.2 Create Sample Dataset
This paper focuses on discipline evaluation in
universities and colleges of Shanghai. It makes
discipline classification in Shanghai. According to
the result of China Discipline Ranking from
CDGDC, disciplines in universities and colleges of
Shanghai are defined as 4 categories, outstanding
disciplines, advantageous disciplines, newly-
emerging disciplines and general disciplines.
Classification attributes are created in database. For
a few disciplines which were not evaluated in China
Discipline Ranking in 2012, they are evaluated
comprehensively by experts from universities and
from educational administrative department of
Shanghai according to Discipline Evaluation
Indicator System from CDGDC in 2012. Their
Discipline Decision Tree Classification Algorithm and Application based on Weighted Information Gain Ratio
81

classification attributes are also created in database.
The sample of training dataset is shown in Table
3. 98 disciplines from 26 universities and colleges of
Shanghai are selected as samples in training dataset.
Attributes A
i
(i = 1, 2, …, 76) are defined as
evaluation attributes in Table 2 of Table Structure of
Discipline Basic Information. The Discipline
Decision Tree Classification Algorithm based on
Weighted Information Gain Ratio is applied to train
the sample dataset.
Table 3: Sample of training dataset.
EvaluationAttributes Classification
… A
i
A
j
A
k
…
Outstanding
… >80
high
>16 …
Advantageous
… <=20
high
10..
16
…
Newly‐emerging
… 21..50
medi
>16 …
Advantageous
… 51..80
high
10..
16
…
Newly‐emerging
… <=20
medi
<10 …
General
… 21..50
low
10..
16
…
Newly‐emerging
… 51..80
high
10..
16
…
Advantageous
… >80
high
>16 …
General
… <=20
low
<10 …
General
… 51..80
high
>16 …
Advantageous
… 51..80
high
>16 …
Outstanding
… 21..50
medi
<10 …
Newly‐emerging
… 51..80
medi
>16 …
Advantageous
… <=20
low
10..
16
…
General
… >80
medi
>16 …
Advantageous
… >80
medi
>16 …
Advantageous
… 21..50
low
10..
16
…
General
… <=20
low
<10 …
General
… 51..80
high
>16 …
Newly‐emerging
… … … … … …
21..50
medi
10..
16
…
Advantageous
>80
high
<10 …
Newly‐emerging
>80
medi
>16 …
Advantageous
21..50
high
<10 …
General
4.3.3 Create Discipline Classification Rules
When running the automatic discipline classification
system, the prediction model extracts samples from
training dataset, and creates discipline classification
decision tree by the algorithm. It prunes the decision
tree by the post-pruning algorithm based on
Bayesian theory (Cai, 2011). Figure 2 shows parts of
the decision tree.
Classification rules are created by the decision
tree. Figure 3 shows parts of the classification rules.
Figure 2: Discipline classification decision tree.
Figure 3: Discipline classification rules.
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82

4.3.4 Evaluate and Optimize the
Classification Rules
How to evaluate the classification rules is important.
Testing dataset is used to evaluate the Discipline
Decision Tree Classification Algorithm. We
compare the predicting results with the actual
results. It is effective if our scheme can achieve
about 80% accuracy in forecasts.
The first-class disciplines of universities and
colleges in Shanghai are selected as testing dataset,
which contains 161 disciplines in 22 universities
(
Shanghai Municipal Government, 2012). 60 typical
items are imported to test the classification rules.
Testing result is shown in Figure 4. 49 prediction
results are correct. We achieve about 81.67%
accuracy in forecasts. Coincidence matrix compares
difference between prediction value and actual
value. Confidence report is analyzes the accuracy of
prediction.
Figure 4: Evaluation result of discipline classification
rules.
After evaluating and analysing classification
rules, t, the correlation coefficient, shall be adjusted
slightly to get the optimized decision tree and
classification rules. We can raise the accuracy to
83.33%. The optimized result is shown in Figure 5.
Figure 5: Evaluation result of optimized classification
rules.
4.4 Use Automatic Discipline
Classification System to Do
Dynamic Evaluation
The automatic discipline classification system based
on weighted information gain ratio classifies
discipline according to objective data instead of
subjective assumption. It establishes foundation for
the objective discipline quality system. It initializes
and monitors the disciplines dynamically. The
educational administrative department can easily
understand the layout structure and status.
5 CONCLUSIONS
This paper proposes an algorithm of discipline
decision tree classification based on weighted
information gain ratio on the basis of extensive
investigation and careful analysis of the existing
evaluation methods. An automatic discipline
classification system is established to analyze the
discipline samples in universities and colleges of
Shanghai. The study and application of the
algorithm is helpful to reveal the development
tendency of disciplines. It predicts the progress and
breakthrough of disciplines in the future. Meanwhile
it provides basis for the educational administrative
department to develop a new round discipline
strategy.
In the future, we will take further research on
fuzzy decision tree according to the discipline
characteristics. Fuzzy classification rules will be
deduced to evaluate disciplines more scientifically.
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