3,979 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Phoneme and sentence-level ensembles for speech recognition
We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level and one at the utterance level, with a phoneme-level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal, while bagging significantly outperforms all other methods. We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition
An empirical comparison of supervised machine learning techniques in bioinformatics
Research in bioinformatics is driven by the experimental data.
Current biological databases are populated by vast amounts of
experimental data. Machine learning has been widely applied to
bioinformatics and has gained a lot of success in this research
area. At present, with various learning algorithms available in the
literature, researchers are facing difficulties in choosing the best
method that can apply to their data. We performed an empirical
study on 7 individual learning systems and 9 different combined
methods on 4 different biological data sets, and provide some
suggested issues to be considered when answering the following
questions: (i) How does one choose which algorithm is best
suitable for their data set? (ii) Are combined methods better than
a single approach? (iii) How does one compare the effectiveness
of a particular algorithm to the others
Boosting Applied to Word Sense Disambiguation
In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied
to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of
15 selected polysemous words show that the boosting approach surpasses Naive
Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy
on supervised WSD. In order to make boosting practical for a real learning
domain of thousands of words, several ways of accelerating the algorithm by
reducing the feature space are studied. The best variant, which we call
LazyBoosting, is tested on the largest sense-tagged corpus available containing
192,800 examples of the 191 most frequent and ambiguous English words. Again,
boosting compares favourably to the other benchmark algorithms.Comment: 12 page
Efficient Intrusion Detection Model Using Ensemble Methods
Ensemble method or any combination model train multiple learners to solve the classification or regression problems, not by simply ordinary learning approaches that can able to construct one learner from training data rather construct a set of learners and combine them. Boosting algorithm is one of the most important recent developments in the area of classification methodology. Boosting belongs to a family of algorithms that has the capability to convert a group of weak learners to strong learners. Boosting works in a sequential manner by adding a classification algorithm to the next updated weight of the training samples by doing the majority voting technique of the sequence of classifiers. The boosting method combines the weak models to produce a powerful one and reduces the bias of the combined model. AdaBoost algorithm is the most influential algorithm that efficiently combines the weak learners to generate a strong classifier that could be able to classify a training data with better accuracy. AdaBoost differs from the current existing boosting methods in detection accuracy, error cost minimization, computational time and detection rate. Detection accuracy and computational cost are the two main metrics used to analyze the performance of AdaBoost classification algorithm. From the simulation result, it is evident that AdaBoost algorithm could able to achieve high detection accuracy with less computational time, and minimum cost compared to a single classifier. We have proposed a predictive model to classify normal class and attack class and an online inference engine is being imposed, either to allow or deny access to a network
- …