1. Graph-Based Approaches for Over-Sampling in the Context of Ordinal
Regression
Abstract:
The classification of patterns into naturally ordered labels is referred to as
ordinal regression or ordinal classification. Usually, this classification
setting is by nature highly imbalanced, because there are classes in the
problem that are a priori more probable than others. Although standard
over-sampling methods can improve the classification of minority classes
in ordinal classification, they tend to introduce severe errors in terms of the
ordinal label scale, given that they do not take the ordering into account. A
specific ordinal over-sampling method is developed in this paper for the
first time in order to improve the performance of machine learning
classifiers. The method proposed includes ordinal information by
approaching over-sampling from a graph-based perspective. The results
presented in this paper show the good synergy of a popular ordinal
regression method (a reformulation of support vector machines) with the
graph-based proposed algorithms, and the possibility of improving both
the classification and the ordering of minority classes. A cost-sensitive
version of the ordinal regression method is also introduced and compared
with the over-sampling proposals, showing in general lower performance
for minority classes.

2. Existing System:
Ordinal classification problems arise in several areas such as economy,
medicine or image ranking, to name a few. For an explanatory example,
consider the case of financial trading where an agent intends to predict not
only whether to buy an asset, but also the amount of investment. The
different situations could be categorised as {“no investment”, “little
investment”, “big investment”, “huge investment”}. In this case, the
natural order among the classes can be appreciated, as well as the necessity
of penalising differently the misclassification errors (it should not be
considered equal misclassifying a “no investment” instance with a “huge
investment” one than misclassifying.
Proposed System:
The proposed methods are used in conjunction with the well-known
SMOTE algorithm and a popular reformulation of the support vector
machine paradigm (SVM) for ordinal classification. This classifier has been
chosen because it is one of the most successful, well known and widely
used in this context, despite the fact that the usual formulation of the soft-
margin maximization paradigm is focused on improving overall
performance, consequently harming the classification of minority classes.
Hardware Requirements:

3. • System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server