students:phd_mlws
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| ====== Machine Learning Workflow System ====== | ====== Machine Learning Workflow System ====== | ||
| + | This subject is proposed as part of the [[http:// | ||
| ===== Context ===== | ===== Context ===== | ||
| For many years, Machine Learning research has been focusing on designing new algorithms for solving similar kinds of problem instances (Kotthoff, 2016). However, Researchers have long ago recognized that a single algorithm will not give the best performance across all problem instances, e.g. the No-Free-Lunch-Theorem (Wolpert, 1996) states that the best classifier will not be the same on every dataset. Consequently, | For many years, Machine Learning research has been focusing on designing new algorithms for solving similar kinds of problem instances (Kotthoff, 2016). However, Researchers have long ago recognized that a single algorithm will not give the best performance across all problem instances, e.g. the No-Free-Lunch-Theorem (Wolpert, 1996) states that the best classifier will not be the same on every dataset. Consequently, | ||
| - | To support automatic selection of algorithms, Portfolio approaches aim at performing per-instance algorithm selection (Leyton et al., 2003). When portfolio refers to more complex products than algorithms (i.e. not only a set of software components but the composition of a set of consistent software components), | + | To support automatic selection of algorithms, Portfolio approaches aim at performing per-instance algorithm selection (Leyton et al., 2003). When a portfolio refers to more complex products than algorithms (i.e. not only a set of software components but the composition of a set of consistent software components), |
| A Machine Learning (ML) Workflow can be defined as a tuple (h,p,c) where h represents hyper-parameter tuning strategy, | A Machine Learning (ML) Workflow can be defined as a tuple (h,p,c) where h represents hyper-parameter tuning strategy, | ||
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| * The structural characteristics (size, quality, and nature) of the collected data | * The structural characteristics (size, quality, and nature) of the collected data | ||
| * How the results will be used. | * How the results will be used. | ||
| - | This task is highly complex because of the increasing number of available algorithms, the difficulty in choosing the correct preprocessing techniques together with the right algorithms as well as the correct tuning of their parameters. To decide which algorithm to choose, data scientists often consider families of algorithms in which they are experts, and can leave aside algorithms that are more “exotic” to them, but could perform better for the problem they are trying to solve. | + | This task is highly complex because of the increasing number of available algorithms, the difficulty in choosing the correct preprocessing techniques together with the right algorithms as well as the correct tuning of their parameters |
| ROCKFlows | ROCKFlows | ||
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| The approach is thus positioned differently from the platforms that help select the workflows components like Weka (Hall et al, 2009), Orange (Demsar et al, 2004), KNIME (Berthold et al, 2007), RapidMiner (Mierswa et al, 2006) or ClowdFlows (Kranjc et al, 2012). Indeed, such platforms have a lot of components that can be selected to create the desired workflows, but if these systems are useful for data scientists, they can be too complex and overwhelming for non-expert users. For such users, it may be more helpful to use a system that either provides them directly with the workflow to use, or at least suggests the components of the workflow to use for their specific problem. | The approach is thus positioned differently from the platforms that help select the workflows components like Weka (Hall et al, 2009), Orange (Demsar et al, 2004), KNIME (Berthold et al, 2007), RapidMiner (Mierswa et al, 2006) or ClowdFlows (Kranjc et al, 2012). Indeed, such platforms have a lot of components that can be selected to create the desired workflows, but if these systems are useful for data scientists, they can be too complex and overwhelming for non-expert users. For such users, it may be more helpful to use a system that either provides them directly with the workflow to use, or at least suggests the components of the workflow to use for their specific problem. | ||
| - | The interest of such a system is so valuable that big companies have proposed their own knowledge flows. The IBM Watson platform offers an interface to analyze unstructured data (for example text files) and takes as input, questions in plain English. Amazon’s product is a black box similar to the IBM’s platform but it is focused on supervised Machine Learning, in particular on classification and regression tasks. The workflow is built automatically by the platform by analyzing the input data. On the other hand, the solution proposed by Microsoft Azure, instead of choosing the solution automatically for the user, it provides advices to the users on which components to use to form the workflow. These advices are based both on the best practices used in Machine Learning and on the algorithms available on the platform, that is, they cover a limited part of possible Machine Learning workflows. For example, to solve the clustering problem, only the K-means algorithm is proposed, while the limitations of this technique are well-known and several families of clustering algorithms have been proposed to overcome these. | + | The interest of such a system is so valuable that big companies have proposed their own knowledge flows. The IBM Watson platform offers an interface to analyze unstructured data (for example text files) and takes as input, questions in plain English. Amazon’s product is a black box similar to the IBM’s platform but it is focused on supervised Machine Learning, in particular on classification and regression tasks. The workflow is built automatically by the platform by analyzing the input data. On the other hand, the solution proposed by Microsoft Azure, instead of choosing the solution automatically for the user, provides advices to the users on which components to use to form the workflow. These advices are based both on the best practices used in Machine Learning and on the algorithms available on the platform, that is, they cover a limited part of possible Machine Learning workflows. For example, to solve the clustering problem, only the K-means algorithm is proposed, while the limitations of this technique are well-known and several families of clustering algorithms have been proposed to overcome these. |
| ===== Objectives ===== | ===== Objectives ===== | ||
| - | The main objective of this thesis is to explore the alliance between a portfolio and a SPL to automatically propose ML workflows according to end-user problems. So the SPL is the link between the portfolio and the end-user. It manages the identification of the end-user problem. It proposes solutions among which end-user chooses according to her own criteria. It generates the corresponding codes and, it could launch the experiment. It must be able to collect the results of the experiments to get feedbacks and eventually to enrich the platform. | + | The main objective of this thesis is to explore the alliance between a portfolio and a SPL to automatically propose ML workflows according to end-user problems. So the SPL is the link between the portfolio and the end-user. It manages the identification of the end-user problem. It proposes solutions among which the end-user chooses according to her own criteria. It generates the corresponding codes and, it could launch the experiment. It must be able to collect the results of the experiments to get feedbacks and eventually to enrich the platform. |
| The thesis must address the following challenges: Relevance and quality of predictions and Scalability to manage the huge mass of ML workflows. | The thesis must address the following challenges: Relevance and quality of predictions and Scalability to manage the huge mass of ML workflows. | ||
| To meet these challenges, attention should be paid to the following aspects: | To meet these challenges, attention should be paid to the following aspects: | ||
| - | * //Handling Variabilities: | + | * //Handling Variabilities: |
| - | *// Architecture of portfolio// | + | *// Architecture of the portfolio |
| - | * //Handling Scalability of Portfolio: | + | * //Handling Scalability of the Portfolio: //Selecting |
| - | * //Ensuring global consistency// | + | * //Ensuring global consistency// |
| - | We have a two-year experience on this subject which has enabled us to (I) eliminate some approaches (e.g. modeling knowledge as a system of constraints because it generates on our current basis more than 6 billion constraints), | + | We have a two-year experience on this subject which has enabled us to (I) eliminate some approaches (e.g. modeling knowledge as a system of constraints because it generates on our current basis more than 6 billion constraints), |
| The thesis must investigate the research around the selection of algorithms, considering the automatic composition of workflows and supporting dynamic evolutions. It is therefore a thesis in software engineering research but to address one of the current most central problems in machine learning. | The thesis must investigate the research around the selection of algorithms, considering the automatic composition of workflows and supporting dynamic evolutions. It is therefore a thesis in software engineering research but to address one of the current most central problems in machine learning. | ||
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| Rice JR (1976) The Algorithm Selection Problem. Adv Comput 15: | Rice JR (1976) The Algorithm Selection Problem. Adv Comput 15: | ||
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| + | Martin Salvador M, Budka M, Gabrys B (2016) Towards automatic composition of multicomponent predictive systems. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). doi: 10.1007/ | ||
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| + | Serban F, Vanschoren J, Kietz J-U, Bernstein A (2013) A survey of intelligent assistants for data analysis. ACM Comput Surv. doi: 10.1145/ | ||
| Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7): | Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7): | ||
students/phd_mlws.1495264290.txt.gz · Last modified: 2017/05/20 07:11 by blay