Abstract: One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection.
Abstract: This paper presents a classifier ensemble approach for
predicting the survivability of the breast cancer patients using the
latest database version of the Surveillance, Epidemiology, and End
Results (SEER) Program of the National Cancer Institute. The system
consists of two main components; features selection and classifier
ensemble components. The features selection component divides the
features in SEER database into four groups. After that it tries to find
the most important features among the four groups that maximizes the
weighted average F-score of a certain classification algorithm. The
ensemble component uses three different classifiers, each of which
models different set of features from SEER through the features
selection module. On top of them, another classifier is used to give
the final decision based on the output decisions and confidence
scores from each of the underlying classifiers. Different classification
algorithms have been examined; the best setup found is by using the
decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the
underlying classifiers and Na¨ıve Bayes for the classifier ensemble
step. The system outperforms all published systems to date when
evaluated against the exact same data of SEER (period of 1973-2002).
It gives 87.39% weighted average F-score compared to 85.82% and
81.34% of the other published systems. By increasing the data size to
cover the whole database (period of 1973-2014), the overall weighted
average F-score jumps to 92.4% on the held out unseen test set.
Abstract: Learning using labeled and unlabelled data has
received considerable amount of attention in the machine learning
community due its potential in reducing the need for expensive
labeled data. In this work we present a new method for combining
labeled and unlabeled data based on classifier ensembles. The model
we propose assumes each classifier in the ensemble observes the
input using different set of features. Classifiers are initially trained
using some labeled samples. The trained classifiers learn further
through labeling the unknown patterns using a teaching signals that is
generated using the decision of the classifier ensemble, i.e. the
classifiers self-supervise each other. Experiments on a set of object
images are presented. Our experiments investigate different classifier
models, different fusing techniques, different training sizes and
different input features. Experimental results reveal that the proposed
self-supervised ensemble learning approach reduces classification
error over the single classifier and the traditional ensemble classifier
approachs.
Abstract: As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.