Recent years have seen a great inclination towards Machine Learning classification and researchers are thinking in terms of achieving accuracy and correctness. Many studied have proved that an ensemble of classifiers ...
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ISBN:
(纸本)9780769551449
Recent years have seen a great inclination towards Machine Learning classification and researchers are thinking in terms of achieving accuracy and correctness. Many studied have proved that an ensemble of classifiers outperform individual ones in terms of accuracy. Qamar et al. have developed a Similarity Learning Algorithm (SiLA) based on a combination of k nearest neighbor algorithm and Voted Perceptron. This approach is different from other state of the art algorithms in the sense that it learns appropriate similarity metrics rather than distancebased ones for all types of datasets i.e. textual as well as nontextual. In this paper, we present a novel ensemble classifier RotSiLA which is developed by combining Rotation Forest algorithm and SiLA. The Rot-SiLA ensemble classifier is built upon two types of approaches;one based on standard kNN and another based on symmetric kNN (SkNN), just as was the case with SiLA algorithm. It has been observed that Rot-SiLA ensemble outperforms other variants of the Rotation Forest ensemble as well as SiLA significantly when experiments were conducted with 14 UCI repository data sets. The significance of the results was determined by s-test.
Drug design datasets are usually known as hard-modeled, having a large number of features and a small number of samples. Regression types of problems are common in the drug design area. Committee machines (ensembles) ...
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Drug design datasets are usually known as hard-modeled, having a large number of features and a small number of samples. Regression types of problems are common in the drug design area. Committee machines (ensembles) have become popular in machine learning because of their good performance. In this study, the dynamics of ensembles used in regression-related drug design problems are investigated with a drug design dataset collection. The study tries to determine the most successful ensemble algorithm, the base algorithm-ensemble pair having the best/worst results, the best successful single algorithm, and the similarities of algorithms according to their performances. We also discuss whether ensembles always generate better results than single algorithms.
This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chose...
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This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity;the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.
ensemble algorithms have been historically categorized into two separate paradigms, boosting and random forests, which differ significantly in the way each ensemble is constructed. Boosting algorithms represent one ex...
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ensemble algorithms have been historically categorized into two separate paradigms, boosting and random forests, which differ significantly in the way each ensemble is constructed. Boosting algorithms represent one extreme, where ail iterative greedy optimization strategy, weak learners (e.g., small classification trees), and stage weights are employed to target difficult-to-classify regions in the training space. Oil the other extreme, random forests rely on randomly selected features and complex learners (learners that exhibit low bias, e.g., large regression trees) to classify well over the entire training data. Because the approach is not targeting the next learner For inclusion, it tends to provide a natural robustness to noisy labels. In this work, we introduce the ensemble bridge algorithm, which is capable of transitioning between boosting and random forests using a regularization parameter nu is an element of [0, 1]. Because the ensemble bridge algorithm is a compromise between the greedy nature of boosting and the randomness present in random forests, it yields robust performance in the presence of a noisy response and superior performance in the presence of a clean response. Often, drug discovery data (e.g., computational chemistry data) have varying levels of noise. Hence, this method enables a practitioner to employ a single method to evaluate ensemble performance. The method's robustness is verified across a variety of data sets where the algorithm repeatedly yields better performance than either boosting or random forests alone. Finally, we provide diagnostic tools for the new algorithm, including a measure of variable importance and ail observational Clustering tool.
Gene expression ratios obtained from microarray images are strongly affected by the algorithms used to process them as well as by the quality of the images. Hundreds of spots often suffer from quality problems caused ...
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ISBN:
(纸本)9783642169519
Gene expression ratios obtained from microarray images are strongly affected by the algorithms used to process them as well as by the quality of the images. Hundreds of spots often suffer from quality problems caused by the manufacturing process and many must be discarded because of lack of reliability. Recently, several computational models have been proposed in the literature to identify defective spots, including the powerful Support Vector Machines (SVMs). In this paper we propose to use different strategies based on aggregation methods to classify the spots according to their quality. On one hand we apply an ensemble of classifiers, in particular three boosting methods, namely Discrete, Real and Gentle AdaBoost. As we use a public dataset which includes the subjective labeling criteria of three human experts, we also evaluate different ways of modeling consensus between the experts. We show that for this problem ensembles achieve improved classification accuracies over alternative state-of-the-art methods.
Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. A well known algorithm based on boosting is AdaBoost. Boosting At Start (BAS) is a boosting framework th...
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ISBN:
(纸本)9781424447053
Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. A well known algorithm based on boosting is AdaBoost. Boosting At Start (BAS) is a boosting framework that generalizes AdaBoost by allowing any initial weight distribution. BAS Committee is a scheme that uses feature clustering to determine the best weight assignments in the BAS framework. One of the drawbacks of BAS Committee is its final step which uses a simple Majority Voting approach over the chosen classifiers. Entropy Guided Transformation Learning (ETL) is a machine learning strategy that combines Decision Trees and Transformation Based Learning avoiding the explicit need of Template Design. Here, we present ETL Voting BAS Committee, a scheme that combines ETL and BAS Committee in order to determine the best combination for the classifiers of the ensemble. Besides that, since no extra assumption is made, ETL Voting is generic and can be used in any committee approach. Our empirical findings indicate that the BAS performance can be improved with a new combination of the classifiers determined by ETL Voting.
Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. A well known algorithm based on boosting is AdaBoost. Boosting At Start (BAS) is a boosting framework th...
详细信息
Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. A well known algorithm based on boosting is AdaBoost. Boosting At Start (BAS) is a boosting framework that generalizes AdaBoost by allowing any initial weight distribution. BAS Committee is a scheme that uses feature clustering to determine the best weight assignments in the BAS framework. One of the drawbacks of BAS Committee is its final step which uses a simple Majority Voting approach over the chosen classifiers. Entropy Guided Transformation Learning (ETL) is a machine learning strategy that combines Decision Trees and Transformation Based Learning avoiding the explicit need of Template Design. Here, we present ETL Voting BAS Committee, a scheme that combines ETL and BAS Committee in order to determine the best combination for the classifiers of the ensemble. Besides that, since no extra assumption is made, ETL Voting is generic and can be used in any committee approach. Our empirical findings indicate that the BAS performance can be improved with a new combination of the classifiers determined by ETL Voting.
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