Solving Regression Problems Using Competitive Ensemble Models

Frayman, Yakov; Rolfe, Bernard; Webb, Geoffrey I.

doi:10.1007/3-540-36187-1_45

Cited by 11 publications

(8 citation statements)

References 21 publications

(21 reference statements)

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“…Each classification method has its own strengths and weakness; the ensemble of similar classifiers would inherit such benefits and drawbacks. The four classification methods used in this paper have different advantages, it is useful to construct a consensus model by summarizing different pattern [ 33 ]. Here, the obtained classifiers' results in above section are fed into the second layer SVM to get the final result.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of approved drugs from experimental drugs by learning methods

Tang

Zhu

et al. 2011

BMC Bioinformatics

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BackgroundTo assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.ResultsFour methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.ConclusionThe studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.

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Section: Resultsmentioning

confidence: 99%

Discrimination of approved drugs from experimental drugs by learning methods

Tang

Zhu

et al. 2011

BMC Bioinformatics

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“…All the three models, along with their ensemble (a combination of MIMO, MISO, and RNN), were tested to find the model which gives the best accuracy along with other performance measures. The ensemble model is a committee model which uses the voting mechanism for prediction [ 25 , 26 , 27 ]. If the majority votes for the ON state, it predicts ON and if the majority votes for the OFF state, it predicts OFF.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

Khan

Ghani

Haider

2018

Sensors

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IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

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“…Abaixo mostraremos como podemos combinar os resultados das redes descritas na última sec ¸ão, usando um método de Ensemble, que combina os vários modelos treinados para resolver um mesmo problema. Essse ensemble foi feito inspirado em [39], que mostraram uma grande melhoria nos resultados quando várias redes são combinadas numa regressão logística. O uso de Ensemble tem se mostrado uma importante técnica no desenvolvimento de modelos de machine learning ainda nos anos 90, quando Hansen & Salamon [40] mostraram que as predic ¸ões feitas pela combinac ¸ão de um conjunto de classificadores são frequentemente mais precisas do que as feitas somente pelo melhor classificador.…”

Section: Ensembleunclassified

Classificação Morfológica de Galáxias no S-PLUS por Combinação de Redes Convolucionais

Cardoso¹,

Schwarz²,

Dias³

et al. 2021

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The universe is composed of galaxies that have diverse shapes. Once the structure of a galaxy is determined, it is possible to obtain important information about its formation and evolution. Morphologically classifying galaxies means cataloging them according to their visual appearance and the classification is linked to the physical properties of the galaxy. A morphological classification made through visual inspection is subject to biases introduced by subjective observations made by human volunteers. For this reason, systematic, objective and easily reproducible classification of galaxies has been gaining importance since the astronomer Edwin Hubble created his famous classification method. In this work, we combine accurate visual classifications of the Galaxy Zoo project with Deep Learning methods. The goal is to find an efficient technique at human performance level classification, but in a systematic and automatic way, for classification of elliptical and spiral galaxies. For this, a neural network model was created through an Ensemble of four other convolutional models, allowing a greater accuracy in the classification than what would be obtained with any one individual. Details of the individual models and improvements made are also described. The present work is entirely based on the analysis of images (not parameter tables) from DR1 (www.datalab.noao.edu) of the Southern Photometric Local Universe Survey (S-PLUS). In terms of classification, we achieved, with the Ensemble, an accuracy of ≈ 99% in the test sample (using pre-trained networks).

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Solving Regression Problems Using Competitive Ensemble Models

Cited by 11 publications

References 21 publications

Discrimination of approved drugs from experimental drugs by learning methods

Discrimination of approved drugs from experimental drugs by learning methods

Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

Classificação Morfológica de Galáxias no S-PLUS por Combinação de Redes Convolucionais

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