1 research outputs found
J-model: an open and social ensemble learning architecture for classification
Ensemble learning is a promising direction of research in machine learning, in which an ensemble
classifier gives better predictive and more robust performance for classification problems
by combining other learners. Meanwhile agent-based systems provide frameworks to
share knowledge from multiple agents in an open context. This thesis combines multi-agent
knowledge sharing with ensemble methods to produce a new style of learning system for open
environments.
We now are surrounded by many smart objects such as wireless sensors, ambient communication
devices, mobile medical devices and even information supplied via other humans. When
we coordinate smart objects properly, we can produce a form of collective intelligence from
their collaboration. Traditional ensemble methods and agent-based systems have complementary
advantages and disadvantages in this context. Traditional ensemble methods show better
classification performance, while agent-based systems might not guarantee their performance
for classification. Traditional ensemble methods work as closed and centralised systems
(so they cannot handle classifiers in an open context), while agent-based systems are natural
vehicles for classifiers in an open context.
We designed an open and social ensemble learning architecture, named J-model, to merge the
conflicting benefits of the two research domains. The J-model architecture is based on a service
choreography approach for coordinating classifiers. Coordination protocols are defined by
interaction models that describe how classifiers will interact with one another in a peer-to-peer
manner. The peer ranking algorithm recommends more appropriate classifiers to participate in
an interaction model to boost the success rate of results of their interactions. Coordinated participant
classifiers who are recommended by the peer ranking algorithm become an ensemble
classifier within J-model.
We evaluated J-model’s classification performance with 13 UCI machine learning benchmark
data sets and a virtual screening problem as a realistic classification problem. J-model showed
better performance of accuracy, for 9 benchmark sets out of 13 data sets, than 8 other representative
traditional ensemble methods. J-model gave better results of specificity for 7 benchmark
sets. In the virtual screening problem, J-model gave better results for 12 out of 16 bioassays
than already published results. We defined different interaction models for each specific classification
task and the peer ranking algorithm was used across all the interaction models.
Our research contributions to knowledge are as follows. First, we showed that service choreography
can be an effective ensemble coordination method for classifiers in an open context. Second, we used interaction models that implement task specific coordinations of classifiers to
solve a variety of representative classification problems. Third, we designed the peer ranking
algorithm which is generally and independently applicable to the task of recommending appropriate
member classifiers from a classifier pool based on an open pool of interaction models
and classifiers