A New Fuzzy ARTMAP Approach for Predicting Biological Activity of Potential HIV-1 Protease Inhibitors

Andonie, Răzvan; Fabry-Asztalos, Levente; Magill, Lukas; Abdul-Wahid, S.

doi:10.1109/bibm.2007.9

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Bayesian techniques are implemented by packages like BayesianOptimization 4 , Spearmint 5 , and pyGPGO 6 .…”

Section: Software For Hyperparameter Optimizationmentioning

confidence: 99%

“…In [6] we also optimized not only the relevances and the order of the training data presentation but also some hyperparameters of the FAMR network. We used again a genetic algorithm.…”

Section: Hyperparameter Optimization In Fuzzy Artmap Modelsmentioning

confidence: 99%

“…https ://githu b.com/fmfn/Bayes ianOp timiz ation 5. https ://githu b.com/HIPS/Spear mint 6. https ://githu b.com/hawk3 1/pyGPG O 7.…”

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confidence: 99%

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Hyperparameter optimization in learning systems

Andonie

2019

J Membr Comput

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While the training parameters of machine learning models are adapted during the training phase, the values of the hyperparameters (or meta-parameters) have to be specified before the learning phase. The goal is to find a set of hyperparameter values which gives us the best model for our data in a reasonable amount of time. We present an integrated view of methods used in hyperparameter optimization of learning systems, with an emphasis on computational complexity aspects. Our thesis is that we should solve a hyperparameter optimization problem using a combination of techniques for: optimization, search space and training time reduction. Case studies from real-world applications illustrate the practical aspects. We create the framework for a future separation between parameters and hyperparameters in adaptive P systems.

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“…Bayesian techniques are implemented by packages like BayesianOptimization 4 , Spearmint 5 , and pyGPGO 6 .…”

Section: Software For Hyperparameter Optimizationmentioning

confidence: 99%

“…In [6] we also optimized not only the relevances and the order of the training data presentation but also some hyperparameters of the FAMR network. We used again a genetic algorithm.…”

Section: Hyperparameter Optimization In Fuzzy Artmap Modelsmentioning

confidence: 99%

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Hyperparameter optimization in learning systems

Andonie

2019

J Membr Comput

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“…Improving the genetic algorithm can make generalization capability of the relevance data set more efficient [20].…”

Section: A Issues With the Existing Systemmentioning

confidence: 99%

A fuzzy based approach for prediction of biological activities of HIV -1 protease inhibitor compounds

Parthiban¹,

Parthiban

2011

2011 2nd International Conference on Intelligent Agent &Amp; Multi-Agent Systems

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A neuro-fuzzy based approach for predicting the HIV-1 inhibitor compounds activity has been designed when the molecular descriptor attributes of the compound are known. Standard Fuzzy ARTMAP (FAM) is provided with 196 data sets which is divided into 176 training data and 20 test data. The normalized data is given to the FAM network and the result indicates whether the compound is a suitable inhibitor or not.The result analysis is done with/ without GA for the dataset and GA-FAMR algorithm is used to optimize the relevance's assigned to the training data and the accuracy obtained is 93.09%.

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“…In [8] we also focused on the IC  prediction task, using the FAMR model. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair.…”

Section: Our Previous Workmentioning

confidence: 99%

Extreme Data Mining: Inference from Small Datasets

Andonie

2010

INT J COMPUT COMMUN

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Neural networks have been applied successfully in many fields. However, satisfactory results can only be found under large sample conditions. When it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. This deficiency limits the applications of neural network severely. The main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. Several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets. We have the following goals: i. To discuss the meaning of "small" in the context of inferring from small datasets. ii. To overview computational intelligence solutions for this problem. iii. To illustrate the introduced concepts with a real-life application.

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A New Fuzzy ARTMAP Approach for Predicting Biological Activity of Potential HIV-1 Protease Inhibitors

Cited by 6 publications

References 23 publications

Hyperparameter optimization in learning systems

Hyperparameter optimization in learning systems

A fuzzy based approach for prediction of biological activities of HIV -1 protease inhibitor compounds

Extreme Data Mining: Inference from Small Datasets

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