Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

Olson, Randal S.; Moore, Jason H.

doi:10.1007/978-3-319-97088-2_14

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Beyond using GP to perform the machine learning itself, recent work has shown that GP can also be harnessed to optimize a sequence of existing data analysis and machine learning operations on a dataset to maximize the predictive performance of the final machine learning model [30,35]. For example, TPOT 4 is an early prototype that uses GP to optimize a sequence of scikit-learn operations for both classification and regression problems [25][26][27], and has been shown to work quite well across a broad range of application domains ranging from epidemiological studies to image classification to time series prediction [23]. Given the general design of TPOT, the operations it optimizes over can be specialized for particular problem domains.…”

Section: Discussion and Future Workmentioning

confidence: 99%

A System for Accessible Artificial Intelligence

Olson¹,

Sipper²,

Cava³

et al. 2018

Genetic Programming Theory and Practice XV

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While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.

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Section: Discussion and Future Workmentioning

confidence: 99%

A System for Accessible Artificial Intelligence

Olson¹,

Sipper²,

Cava³

et al. 2018

Genetic Programming Theory and Practice XV

Self Cite

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“…Users can also customize and modify the code snippets according to their needs and preferences. Aliro also includes a pre-trained machine learning recommendation system that can assist users to automate the selection of machine learning algorithms and their hyperparameters, as well as provide visualization of the evaluated model and data ( Olson and Moore 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Aliro: an automated machine learning tool leveraging large language models

Choi,

Moran,

Matsumoto

et al. 2023

Bioinformatics

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Motivation Biomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training. Results Aliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data. Availability and implementation Aliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro.

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“…While this was disabled here to prevent information leak (as the meta-learning likely involved datasets used here), this could show benefit for novel datasets. This has also been explored with TPOT [124] but is not yet included in the stable release.…”

Section: Further Analysis and Recommendationsmentioning

confidence: 99%

Population-based Ensemble Learning with Tree Structures for Classification

Evans¹

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<p>Ensemble learning is one of the most powerful extensions for improving upon individual machine learning models. Rather than a single model being used, several models are trained and the predictions combined to make a more informed decision. Such combinations will ideally overcome the shortcomings of any individual member of the ensemble. Most ma- chine learning competition winners feature an ensemble of some sort, and there is also sound theoretical proof to the performance of certain ensem- bling schemes. The benefits of ensembling are clear in both theory and practice. Despite the great performance, ensemble learning is not a trivial task. One of the main difficulties is designing appropriate ensembles. For exam- ple, how large should an ensemble be? What members should be included in an ensemble? How should these members be weighted? Our first contribution addresses these concerns using a strongly-typed population- based search (genetic programming) to construct well-performing ensem- bles, where the entire ensemble (members, hyperparameters, structure) is automatically learnt. The proposed method was found, in general, to be significantly better than all base members and commonly used compari- son methods trialled. With automatically designed ensembles, there is a range of applica- tions, such as competition entries, forecasting and state-of-the-art predic- tions. However, often these applications also require additional prepro- cessing of the input data. Above the ensemble considers only the original training data, however, in many machine learning scenarios a pipeline is required (for example performing feature selection before classification). For the second contribution, a novel automated machine learning method is proposed based on ensemble learning. This method uses a random population-based search of appropriate tree structures, and as such is em- barrassingly parallel, an important consideration for automated machine learning. The proposed method is able to achieve equivalent or improved results over the current state-of-the-art methods and does so in a fraction of the time (six times as fast). Finally, while complex ensembles offer great performance, one large limitation is the interpretability of such ensembles. For example, why does a forest of 500 trees predict a particular class for a given instance? In an effort to explain the behaviour of complex models (such as ensem- bles), several methods have been proposed. However, these approaches tend to suffer at least one of the following limitations: overly complex in the representation, local in their application, limited to particular fea- ture types (i.e. categorical only), or limited to particular algorithms. For our third contribution, a novel model agnostic method for interpreting complex black-box machine learning models is proposed. The method is based on strongly-typed genetic programming and overcomes the afore- mentioned limitations. Multi-objective optimisation is used to generate a Pareto frontier of simple and explainable models which approximate the behaviour of much more complex methods. We found the resulting rep- resentations are far simpler than existing approaches (an important con- sideration for interpretability) while providing equivalent reconstruction performance. Overall, this thesis addresses two of the major limitations of existing ensemble learning, i.e. the complex construction process and the black- box models that are often difficult to interpret. A novel application of ensemble learning in the field of automated machine learning is also pro- posed. All three methods have shown at least equivalent or improved performance than existing methods.</p>

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Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

Cited by 4 publications

References 20 publications

A System for Accessible Artificial Intelligence

A System for Accessible Artificial Intelligence

Aliro: an automated machine learning tool leveraging large language models

Population-based Ensemble Learning with Tree Structures for Classification

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