Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

Olson, Randal S.; Urbanowicz, Ryan J.; Andrews, Peter C.; Lavender, Nicole A.; Kidd, La Creis R.; Moore, Jason H.

doi:10.1007/978-3-319-31204-0_9

Cited by 257 publications

(236 citation statements)

References 16 publications

Supporting

Mentioning

232

Contrasting

Unclassified

Order By: Relevance

“…We then treated all of the materials-based features listed in table 2 as target values to be predicted by surface texture fingerprints, to examine how this single descriptor can be used to model different bulk or surface features of Ag nanoparticles. All the regression models were built, tested and selected using genetic programming (TPOT [27,28]) without human interference, and the result is shown in figure 8. Here we can see that surface texture fingerprints predicts VA with a coefficient of determination R 2 of 94% on testing data set, but performs poorly on bulk features including FCC population, HCP population, ICO population.…”

Section: Classification and Regressionmentioning

confidence: 99%

Texture based image classification for nanoparticle surface characterisation and machine learning

Sun

Barnard

2018

J. Phys. Mater.

View full text Add to dashboard Cite

Restricting materials informatics to the numerical parameters output from conventional materials modelling software restricts us to a subset of machine learning methods capable of uncovering structure/property relationships and driving materials discovery and design. Presented here is a simple way of converting materials structures in to unique image-based fingerprints suitable for image processing methods, that does not require subjective preassessment of the data and selection of descriptors by the user. This combination of methods is shown to classify the morphologies in a set of 425 silver nanoparticles in a meaningful way, and predict the correlation with the energy of the Fermi level in agreement with other machine learning methods that required user intervention. Moving to an image-based, rather than feature list-based, description of nanoparticles and materials brings us one step closer to using experimental micrographs as inputs for machine learning.

show abstract

Section: Classification and Regressionmentioning

confidence: 99%

Texture based image classification for nanoparticle surface characterisation and machine learning

Sun

Barnard

2018

J. Phys. Mater.

View full text Add to dashboard Cite

show abstract

“…Also, the maximum of feature selected was set to be 0.24 of all possible features. VC, DTC and GBC were all provided in the Python Scit-kit learn library [43] . Hyperparameters mentioned above were specified and the rest were left as default.…”

Section: Resultsmentioning

confidence: 99%

Identifying sleep spindles with multichannel EEG and classification optimization

Mei

Grossberg

et al. 2017

Computers in Biology and Medicine

View full text Add to dashboard Cite

Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroencephalography (EEG). Manual classification is time consuming and is susceptible to low inter-rater agreement. This could be improved using an automated approach. This study presents an optimized filter based and thresholding (FBT) model to set up a baseline for comparison to evaluate machine learning models using naïve features, such as raw signals, peak frequency, and dominant power. The FBT model allows us to formally define sleep spindles using signal processing but may miss examples most human scorers would agree are spindles. Machine learning methods in theory should be able to approach performance of human raters but they require a large quantity of scored data, proper feature representation, intensive feature engineering, and model selection. We evaluate both the FBT model and machine learning models with naïve features. We show that the machine learning models derived from the FBT model improve classification performance. An automated approach designed for the current data was applied to the DREAMS dataset [1]. With one of the expert’s annotation as a gold standard, our pipeline yields an excellent sensitivity that is close to a second expert’s scores and with the advantage that it can classify spindles based on multiple channels if more channels are available. More importantly, our pipeline could be modified as a guide to aid manual annotation of sleep spindles based on multiple channels quickly (6–10 seconds for processing a 40-minute EEG recording), making spindle detection faster and more objective.

show abstract

“…In the future, we will assess the true performance of the optimized system over the SemEval-2017 test set, via more thorough automated optimization. We will also compare the performance of our simple system with other similar automated systems (e.g., the TPOT system Olson et al (2016)) in terms of speed and performance.…”

Section: Results Comments and Conclusionmentioning

confidence: 99%

HLP$@$UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors

Sarker¹,

González²

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

View full text Add to dashboard Cite

We present a simple supervised text classification system that combines sparse and dense vector representations of words, and the generalized representations of words via clusters. The sparse vectors are generated from word n-gram sequences (1-3). The dense vector representations of words (embeddings) are learned by training a neural network to predict neighboring words in a large unlabeled dataset. To classify a text segment, the different vector representations of it are concatenated, and the classification is performed using Support Vector Machines (SVMs). Our system is particularly intended for use by nonexperts of natural language processing and machine learning, and, therefore, the system does not require any manual tuning of parameters or weights. Given a training set, the system automatically generates the training vectors, optimizes the relevant hyper-parameters for the SVM classifier, and trains the classification model. We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-Score, our system obtained 8 th position out of 39 submissions (F1-Score: 0.632, average recall: 0.637, accuracy: 0.646).

show abstract

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization

Cited by 257 publications

References 16 publications

Texture based image classification for nanoparticle surface characterisation and machine learning

Texture based image classification for nanoparticle surface characterisation and machine learning

Identifying sleep spindles with multichannel EEG and classification optimization

HLP$@$UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors

Contact Info

Product

Resources

About