191 research outputs found
Metalearning
This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence
Metalearning
This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence
An Overview of the Algorithm Selection Problem
Users of machine learning algorithms need methods that can help them to identify algorithm or their combinations (workflows) that achieve the potentially best performance. Selecting the best algorithm to solve a given problem has been the subject of many studies over the past four decades. This survey presents an overview of the contributions made in the area of algorithm selection problems. We present different methods for solving the algorithm selection problem identifying some of the future research challenges in this domain
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
The results from most machine learning experiments are used for a specific
purpose and then discarded. This results in a significant loss of information
and requires rerunning experiments to compare learning algorithms. This also
requires implementation of another algorithm for comparison, that may not
always be correctly implemented. By storing the results from previous
experiments, machine learning algorithms can be compared easily and the
knowledge gained from them can be used to improve their performance. The
purpose of this work is to provide easy access to previous experimental results
for learning and comparison. These stored results are comprehensive -- storing
the prediction for each test instance as well as the learning algorithm,
hyperparameters, and training set that were used. Previous results are
particularly important for meta-learning, which, in a broad sense, is the
process of learning from previous machine learning results such that the
learning process is improved. While other experiment databases do exist, one of
our focuses is on easy access to the data. We provide meta-learning data sets
that are ready to be downloaded for meta-learning experiments. In addition,
queries to the underlying database can be made if specific information is
desired. We also differ from previous experiment databases in that our
databases is designed at the instance level, where an instance is an example in
a data set. We store the predictions of a learning algorithm trained on a
specific training set for each instance in the test set. Data set level
information can then be obtained by aggregating the results from the instances.
The instance level information can be used for many tasks such as determining
the diversity of a classifier or algorithmically determining the optimal subset
of training instances for a learning algorithm.Comment: 7 pages, 1 figure, 6 table
Learning an Explicit Hyperparameter Prediction Policy Conditioned on Tasks
Meta learning has attracted much attention recently in machine learning
community. Contrary to conventional machine learning aiming to learn inherent
prediction rules to predict labels for new query data, meta learning aims to
learn the learning methodology for machine learning from observed tasks, so as
to generalize to new query tasks by leveraging the meta-learned learning
methodology. In this study, we interpret such learning methodology as learning
an explicit hyperparameter prediction policy shared by all training tasks.
Specifically, this policy is represented as a parameterized function called
meta-learner, mapping from a training/test task to its suitable hyperparameter
setting, extracted from a pre-specified function set called meta learning
machine. Such setting guarantees that the meta-learned learning methodology is
able to flexibly fit diverse query tasks, instead of only obtaining fixed
hyperparameters by many current meta learning methods, with less adaptability
to query task's variations. Such understanding of meta learning also makes it
easily succeed from traditional learning theory for analyzing its
generalization bounds with general losses/tasks/models. The theory naturally
leads to some feasible controlling strategies for ameliorating the quality of
the extracted meta-learner, verified to be able to finely ameliorate its
generalization capability in some typical meta learning applications, including
few-shot regression, few-shot classification and domain generalization.Comment: 59 pages. arXiv admin note: text overlap with arXiv:1904.03758 by
other author
Facilitating and Enhancing the Performance of Model Selection for Energy Time Series Forecasting in Cluster Computing Environments
Applying Machine Learning (ML) manually to a given problem setting is a tedious and time-consuming process which brings many challenges with it, especially in the context of Big Data. In such a context, gaining insightful information, finding patterns, and extracting knowledge from large datasets are quite complex tasks. Additionally, the configurations of the underlying Big Data infrastructure introduce more complexity for configuring and running ML tasks. With the growing interest in ML the last few years, particularly people without extensive ML expertise have a high demand for frameworks assisting people in applying the right ML algorithm to their problem setting. This is especially true in the field of smart energy system applications where more and more ML algorithms are used e.g. for time series forecasting. Generally, two groups of non-expert users are distinguished to perform energy time series forecasting. The first one includes the users who are familiar with statistics and ML but are not able to write the necessary programming code for training and evaluating ML models using the well-known trial-and-error approach. Such an approach is time consuming and wastes resources for constructing multiple models. The second group is even more inexperienced in programming and not knowledgeable in statistics and ML but wants to apply given ML solutions to their problem settings.
The goal of this thesis is to scientifically explore, in the context of more concrete use cases in the energy domain, how such non-expert users can be optimally supported in creating and performing ML tasks in practice on cluster computing environments. To support the first group of non-expert users, an easy-to-use modular extendable microservice-based ML solution for instrumenting and evaluating ML algorithms on top of a Big Data technology stack is conceptualized and evaluated. Our proposed solution facilitates applying trial-and-error approach by hiding the low level complexities from the users and introduces the best conditions to efficiently perform ML tasks in cluster computing environments.
To support the second group of non-expert users, the first solution is extended to realize meta learning approaches for automated model selection. We evaluate how meta learning technology can be efficiently applied to the problem space of data analytics for smart energy systems to assist energy system experts which are not data analytics experts in applying the right ML algorithms to their data analytics problems. To enhance the predictive performance of meta learning, an efficient characterization of energy time series datasets is required. To this end, Descriptive Statistics Time based Meta Features (DSTMF), a new kind of meta features, is designed to accurately capture the deep characteristics of energy time series datasets. We find that DSTMF outperforms the other state-of-the-art meta feature sets introduced in the literature to characterize energy time series datasets in terms of the accuracy of meta learning models and the time needed to extract them. Further enhancement in the predictive performance of the meta learning classification model is achieved by training the meta learner on new efficient meta examples. To this end, we proposed two new approaches to generate new energy time series datasets to be used as training meta examples by the meta learner depending on the type of time series dataset (i.e. generation or energy consumption time series). We find that extending the original training sets with new meta examples generated by our approaches outperformed the case in which the original is extended by new simulated energy time series datasets
MetaBags: Bagged Meta-Decision Trees for Regression
Ensembles are popular methods for solving practical supervised learning
problems. They reduce the risk of having underperforming models in
production-grade software. Although critical, methods for learning
heterogeneous regression ensembles have not been proposed at large scale,
whereas in classical ML literature, stacking, cascading and voting are mostly
restricted to classification problems. Regression poses distinct learning
challenges that may result in poor performance, even when using well
established homogeneous ensemble schemas such as bagging or boosting.
In this paper, we introduce MetaBags, a novel, practically useful stacking
framework for regression. MetaBags is a meta-learning algorithm that learns a
set of meta-decision trees designed to select one base model (i.e. expert) for
each query, and focuses on inductive bias reduction. A set of meta-decision
trees are learned using different types of meta-features, specially created for
this purpose - to then be bagged at meta-level. This procedure is designed to
learn a model with a fair bias-variance trade-off, and its improvement over
base model performance is correlated with the prediction diversity of different
experts on specific input space subregions. The proposed method and
meta-features are designed in such a way that they enable good predictive
performance even in subregions of space which are not adequately represented in
the available training data.
An exhaustive empirical testing of the method was performed, evaluating both
generalization error and scalability of the approach on synthetic, open and
real-world application datasets. The obtained results show that our method
significantly outperforms existing state-of-the-art approaches
Learning Multiple Defaults for Machine Learning Algorithms
The performance of modern machine learning methods highly depends on their
hyperparameter configurations. One simple way of selecting a configuration is
to use default settings, often proposed along with the publication and
implementation of a new algorithm. Those default values are usually chosen in
an ad-hoc manner to work good enough on a wide variety of datasets. To address
this problem, different automatic hyperparameter configuration algorithms have
been proposed, which select an optimal configuration per dataset. This
principled approach usually improves performance, but adds additional
algorithmic complexity and computational costs to the training procedure. As an
alternative to this, we propose learning a set of complementary default values
from a large database of prior empirical results. Selecting an appropriate
configuration on a new dataset then requires only a simple, efficient and
embarrassingly parallel search over this set. We demonstrate the effectiveness
and efficiency of the approach we propose in comparison to random search and
Bayesian Optimization
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