Solving machine learning with machine learning: Exploiting Very Large-Scale Neighbourhood Search for synthesizing machine learning pipelines

This paper presents a comparative study of multiple algorithms that can be used to automatically search for high-performing pipelines on machine learning problems. These algorithms, namely Very Large-Scale Neighbourhood search (VLSN), Breadth-first search, Metropolis-Hastings, Monte-Carlo tree search (MCTS), enumerative A* search, and Genetic...

Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques

Thus far the democratization of machine learning, which resulted in the field of AutoML, has focused on the automation of model selection and hyperparameter optimization. Nevertheless, the need for high-quality databases to increase performance has sparked interest in correlation-based feature selection, a simple and fast, yet effective approach...

Solving ML with ML: Effectiveness of the Metropolis-Hastings algorithm for synthesizing Machine Learning Pipelines

In AutoML, the search space of possible pipelines is often large and multidimensional. This makes it very important to use an efficient search algorithm. We measure the effectiveness of the Metropolis-Hastings algorithm (M-H) in a pipeline synthesis framework, when the search space is described by a context-free grammar. We also compare the...

LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks

The use of learning curves for decision making in supervised machine learning is standard practice, yet understanding of their behavior is rather limited. To facilitate a deepening of our knowledge, we introduce the Learning Curve Database (LCDB), which contains empirical learning curves of 20 classification algorithms on 246 datasets. One of...

Evolutionary neural cascade search across supernetworks

To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage super-networks whose subnetworks encode candidate neural network architectures. These...

Development of a Catalysis Analytics Platform: Enabling Machine Learning in Catalyst Discovery

Recently machine learning (ML) has become increasingly popular, and has been shown to be a powerful predictive technique. The applications of ML cover a wide range of disciplines, including the natural sciences. Presently, the field of catalysis is still relatively unexposed to ML and other data-driven techniques. This can largely be attributed...

AutoML: Towards automation of machine learning systems maintainability

Machine learning systems both gained significant interest from the academic side and have seen adoption in the industry. However, one aspect that has received insufficient attention so far is the study of the lifecycle of such systems. This aspect is particularly important due to various ML systems' strong dependency on data, which is...

Do More Elaborate Search Strategies Lead to Better Neural Architecture Search Performance?

Computer vision tasks, like supervised image classification, are effectively tackled by convolutional neural networks, provided that the architecture, which defines the structure of the network, is set correctly. Neural Architecture Search (NAS) is a relatively young and increasingly popular field that is concerned with automatically optimizing...

AutoML for Forecasting: Learning to tune hybrid forecasting models from previous tasks

A time series is a series of data points indexed in time order. It can represent real world processes, such as demand for groceries, electricity usage and stock prices. Machine Learning (ML) models that accurately forecast these processes enable improved decision-making for reducing waste and increasing efficiency. Previous research has produced...

MultiTune: Dynamic budget allocation for hyperparameter tuning

Hyperparameter optimization(HPO) forms a critical aspect for machine learning applications to attain superior performance. BOHB (Bayesian Optimization and HyperBand) is a state of the art HPO algorithm that approaches HPO in a multi-armed bandit strategy, augmented with Bayesian optimization to drive configuration sampling. However, BOHB...