Ensemble-based classifiers

Rokach, Lior

doi:10.1007/s10462-009-9124-7

Cited by 2,293 publications

(1,287 citation statements)

References 91 publications

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“…Ensemble methods use multiple learning algorithms to achieve a better performance than the one that could be obtained individually by the constituent methods algorithms alone [10]. A number of options have been used as the 'combining' mechanism of ensemble methods.…”

Section: Using the Proposed Aggregation Methods In Ensemble Modelsmentioning

confidence: 99%

“…Rokach [10] provide the following motivation for doing so: "The main idea behind the ensemble methodology is to weigh several individual classifiers, and combine them in order to obtain a classifier that outperforms every one of them. In fact, human being tends to seek several opinions before making any important decision.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, human being tends to seek several opinions before making any important decision. We weigh the individual opinions, and combine them to reach our final decision" [10]. In an ensemble method for classification purposes the following building blocks can be identified [10]: (i) a training set, (ii) a base inducer (forms a classifier that represents the generalized relationship between the input attributes and the target attribute), (iii) a diversity generator (of diverse classifiers), and (iv) a combiner (of the classifications outputs of the classifiers).…”

Section: Introductionmentioning

confidence: 99%

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IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods

Appel

Chiclana

Carter

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-In the field of Sentiment Analysis, a number of different classifiers are utilised to attempt to establish the polarity of a given sentence. As such, there could be a need for aggregating the outputs of the algorithms involved in the classification effort. If the output of every classification algorithm resembles the opinion of an expert in the subject at hand, we are then in the presence of a group decisionmaking problem, which in turn translates into two subproblems: (a) defining the desired semantic of the aggregation of all opinions, and (b) applying the proper aggregation technique that can achieve the desired semantic chosen in (a). The objective of this article is twofold. Firstly, we present two specific aggregation semantics, namely fuzzy-majority and compensatory, which are based on Induced Ordered Weighted Averaging and Uninorm operators, respectively. Secondly, we show the power of these two techniques by applying them to an existing hybrid method for classification of sentiments at the sentence level. In this case, the proposed aggregation solutions act as a complement in order to improve the performance of the aforementioned hybrid method. In more general terms, the proposed solutions could be used in the creation of semantic-sensitive ensemble methods, instead of the more simple ensemble choices available today in commercial machine learning software offerings.

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Section: Using the Proposed Aggregation Methods In Ensemble Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods

Appel

Chiclana

Carter

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

show abstract

“…Ensemble methods use multiple learning algorithms to improve performance (Rokach 2010). These models have been applied in the hydroinformatics and water resources domain particularly for regression (Solomatine 2008).…”

Section: Ensemble Classifier Approachmentioning

confidence: 99%

Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems

Mounce

Ellis

Edwards

et al. 2017

Water Resour Manage

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Safe, trusted drinking water is fundamental to society. Discolouration is a key aesthetic indicator visible to customers. Investigations to understand discolouration and iron failures in water supply systems require assessment of large quantities of disparate, inconsistent, multidimensional data from multiple corporate systems. A comprehensive data matrix was assembled for a seven year period across the whole of a UK water company (serving three million people). From this a novel data driven tool for assessment of iron risk was developed based on a yearly update and ranking procedure, for a subset of the best quality data. To avoid a 'black box' output, and provide an element of explanatory (human readable) interpretation, classification decision trees were utilised. Due to the very limited number of iron failures, results from many weak learners were melded into one high-quality ensemble predictor using the RUSBoost algorithm which is designed for class imbalance. Results, exploring simplicity vs predictive power, indicate enough discrimination between variable relationships in the matrix to produce ensemble decision tree classification models with good accuracy for iron failure estimation at District Management Area (DMA) scale. Two model variants were explored: 'Nowcast' (situation at end of calendar year) and 'Futurecast' (predict end of next year situation from this year's data). The Nowcast 2014 model achieved 100% True Positive Rate (TPR) and 95.3% True Negative Rate (TNR), with 3.3% of DMAs classified High Risk for un-sampled instances. The Futurecast 2014 achieved 60.5% TPR and 75.9% TNR, with 25.7% of DMAs classified High Risk for un-sampled instances. The output can be used to focus preventive measures to improve iron compliance.

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“…• Tree ensemble learner (TEL) algorithm [11]: This family of methods (also known as Random Forest) are showing very accurate results in different problems in the last years, thus being considered as the state-of-the-art in machine learning. We will use a version based on multiple decision regression trees with bagging and boosting approaches to generate diverse models that will improve aggregate performance.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Energy consumption modeling by machine learning from daily activity metering in a hospital

Ruiz

Pacheco-Torres

Casillas

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Hospitals are large buildings that consume a great amount of energy mostly due to their continuous energy consumption needs, high consumer medical equipment, and special requirements of thermal and air conditions. Reliable dynamic simulation is a chimera because of the complex design and behavior of these buildings. Therefore, monitoring-based methods arise as a plausible alternative. Its main drawback, however, is the lack of enough data to generate statistically robust models.The paper faces this problem thanks to the helpful contribution of a collaborative hospital which was able to generate daily data of electrical energy consumption for a period of six years. Besides, thirteen variables that summarize the daily activity of the hospital are also included. The results show how machine learning techniques generate models that accurately predict the electrical energy consumption based on weather conditions and activity measurements.The obtained results are useful for the design of more specific energy saving strategies, a more efficient economic investment for energy retrofitting of existing buildings and a better management of economic energy cost in large-scale buildings.

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Ensemble-based classifiers

Cited by 2,293 publications

References 91 publications

IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods

IOWA & cross-ratio uninorm operators as aggregation tools in sentiment analysis and ensemble methods

Ensemble Decision Tree Models Using RUSBoost for Estimating Risk of Iron Failure in Drinking Water Distribution Systems

Energy consumption modeling by machine learning from daily activity metering in a hospital

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