| dc.description.abstract | The classification of data with imbalanced class distributions has
posed a significant drawback in the performance attainable by most
well-developed classification systems, which assume relatively
balanced class distributions. This problem is especially crucial
in many application domains, such as medical diagnosis, fraud
detection, network intrusion, etc., which are of great importance
in machine learning and data mining.
This thesis explores meta-techniques which are applicable to most
classifier learning algorithms, with the aim to advance the
classification of imbalanced data. Boosting is a powerful
meta-technique to learn an ensemble of weak models with a promise
of improving the classification accuracy. AdaBoost has been taken
as the most successful boosting algorithm. This thesis starts with
applying AdaBoost to an associative classifier for both learning
time reduction and accuracy improvement. However, the promise of
accuracy improvement is trivial in the context of the class
imbalance problem, where accuracy is less meaningful. The insight
gained from a comprehensive analysis on the boosting strategy of
AdaBoost leads to the investigation of cost-sensitive boosting
algorithms, which are developed by introducing cost items into the
learning framework of AdaBoost. The cost items are used to denote
the uneven identification importance among classes, such that the
boosting strategies can intentionally bias the learning towards
classes associated with higher identification importance and
eventually improve the identification performance on them. Given
an application domain, cost values with respect to different types
of samples are usually unavailable for applying the proposed
cost-sensitive boosting algorithms. To set up the effective cost
values, empirical methods are used for bi-class applications and
heuristic searching of the Genetic Algorithm is employed for
multi-class applications.
This thesis also covers the implementation of the proposed
cost-sensitive boosting algorithms. It ends with a discussion on
the experimental results of classification of real-world
imbalanced data. Compared with existing algorithms, the new
algorithms this thesis presents are superior in achieving better
measurements regarding the learning objectives. | en |
