Download Online Ensemble Learning of Data Streams with

Online Ensemble Learning of Data Streams with
Gradually Evolved Classes
ABSTRACT:
With the rapid development of incremental learning and online learning, mining tasks in the
context of data stream have been widely studied. Generally, data stream mining refers to the
mining tasks that are conducted on a (possibly infinite) sequence of rapidly arriving data records.
As the environment where the data are collected may change dynamically, the data distribution
may also change accordingly. This phenomenon, referred to as concept drift, is one of the most
important challenges in data stream mining. A data stream mining technique should be capable
of constructing and dynamically updating a model in order to learn dynamic changes of data
distributions, i.e., to track the concept drift.
EXISTING SYSTEM:
A novel class-based ensemble approach, namely Class-Based ensemble for Class Evolution
(CBCE), is proposed. In contrast to the abovementioned existing approaches, which process a
data stream in a chunk-by-chunk manner and build a base learner for each chunk, CBCE
maintains a base learner for every class that has ever appeared and updates the base learners
whenever a new example arrives (i.e., in a one-pass manner). Furthermore, a novel undersampling method is also designed to cope with the dynamic class imbalance problem induced by
gradual class evolution.
DISADVANTAGES:

CBCE cannot adapt well to all three cases of class evolution
PROPOSED SYSTEM:
This paper concerns the scenario where classes emerge or disappear gradually. A class-based
ensemble approach, namely Class-Based ensemble for Class Evolution (CBCE), is proposed. By
maintaining a base learner for each class and dynamically updating the base learners with new
data, CBCE can rapidly adjust to class evolution. A novel under-sampling method for the base
learners is also proposed to handle the dynamic class-imbalance problem caused by the gradual
evolution of classes.
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.
ADVANTAGES:

CBCE can adapt well to all three cases of class evolution (i.e., emergence, disappearance
and reoccurrence of classes).

CBCE avoids maintaining a large size of base learners and makes it flexible to class
evolution
MODULES:

UpdateCBModel

ClassEvolutionAdaptation
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System
:
Pentium IV 2.4 GHz.
 Hard Disk
:
40 GB.
 Floppy Drive
:
1.44 Mb.
 Monitor
:
15 VGA Colour.
 Mouse
:
Logitech.
 Ram
:
512 Mb.
 Operating system
:
Windows XP-SP3
 Coding Language
:
ASP.net, C#.net
 Tool
:
Visual Studio
 Database
:
SQL SERVER
SOFTWARE REQUIREMENTS:
Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891 #301, 303 & 304, 3rd Floor,
AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702 Email:
[email protected] | www.takeoffprojects.