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Applying an Ensemble Learning Method for Improving Multi-label Classification Performance

arXiv.org Machine Learning

In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning is to train the base-level algorithms on random subsets of data and then let them vote for the most popular classifications or average the predictions of the base-level algorithms. In this study, an ensemble learning method is proposed for improving multi-label classification evaluation criteria. We have compared our method with well-known base-level algorithms on some data sets. Experiment results show the proposed approach outperforms the base well-known classifiers for the multi-label classification problem.


Adaptive Martingale Boosting

Neural Information Processing Systems

In recent work Long and Servedio LS05short presented a ``martingale boosting'' algorithm that works by constructing a branching program over weak classifiers and has a simple analysis based on elementary properties of random walks. LS05short showed that this martingale booster can tolerate random classification noise when it is run with a noise-tolerant weak learner; however, a drawback of the algorithm is that it is not adaptive, i.e. it cannot effectively take advantage of variation in the quality of the weak classifiers it receives. In this paper we present a variant of the original martingale boosting algorithm and prove that it is adaptive. This adaptiveness is achieved by modifying the original algorithm so that the random walks that arise in its analysis have different step size depending on the quality of the weak learner at each stage. The new algorithm inherits the desirable properties of the original LS05short algorithm, such as random classification noise tolerance, and has several other advantages besides adaptiveness: it requires polynomially fewer calls to the weak learner than the original algorithm, and it can be used with confidence-rated weak hypotheses that output real values rather than Boolean predictions.


Shehroz Khan's answer to What's the difference between adaboost and one layer perceptrons classifier? - Quora

#artificialintelligence

Adaboost is a meta-learning machine learning (ML) algorithm, i.e., it can be used on top of any other ML algorithm. A perceptron classifier is not meta-learning ML. If you have no hidden layer, then perceptron is as good as a linear classifier, if it has one or more hidden layers then it is non-linear classifier. If it is deep (or multiple layers), then hierarchical features can be learned. The output of a perceptron is the linear combination of the feature and their associated weights.


VipBoost: A More Accurate Boosting Algorithm

AAAI Conferences

Boosting is a well-known method for improving the accuracy of many learning algorithms. In this paper, we propose a novel boosting algorithm, VipBoost (voting on boosting classifications from imputed learning sets), which first generates multiple incomplete datasets from the original dataset by randomly removing a small percentage of observed attribute values, then uses an imputer to fill in the missing values.  It then applies AdaBoost (using some base learner) to produce classifiers trained on each of the imputed learning sets, to produce multiple classifiers. The subsequent prediction on a new test case is the most frequent classification from these classifiers. Our empirical results show that VipBoost produces very effective classifiers that significantly improve accuracy for unstable base learners and some stable learners, especially when the initial dataset is incomplete.


Improve Machine Learning Results with Ensemble Learning

#artificialintelligence

NOTE: This article assumes that you are familiar with a basic understanding of Machine Learning algorithms. Suppose you want to buy a new mobile phone, will you walk directly to the first shop and purchase the mobile based on the advice of shopkeeper? You would visit some of the online mobile seller sites where you can see a variety of mobile phones, their specifications, features, and prices. You may also consider the reviews that people posted on the site. However, you probably might also ask your friends and colleagues for their opinions.