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--- students:phd_2019 [2019/05/10 18:38] – [Meta-learning in a Portfolio of Machine Learning Workflows] blay
+++ students:phd_2019 [2019/05/10 18:40] (current) – [Objectives] blay
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 ===== Context =====
- Advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. To determine the right ML workflow for a given problem, numerous parameters have to be taken in account: the kind of data, expected predictions (error, accuracy, time, memory space), the choice of the algorithms and their judicious composition [14,11].
+ Advances in Machine Learning (ML) have brought new solutions for the problems of prediction, decision, and identification. To determine the right ML workflow for a given problem, numerous parameters have to be taken in account: the kind of data, expected predictions (error, accuracy, time, memory space), the choice of the algorithms and their judicious composition [14,11]. \\
-To help with this task, Microsoft Azure Machine Learning, Amazon AWS, and RapidMiner Auto Model[12]  provide ML component assembly tools. However, faced with the complexity of choosing the "right" assembly, meta-learning offers an attractive solution, learning from the problems of the past.  The algorithm selection problem is one of its applications [5]: given a dataset, identify which learning algorithm (and which hyperparameter setting) performs best on it.
+To help with this task, Microsoft Azure Machine Learning, Amazon AWS, and RapidMiner Auto Model[12]  provide ML component assembly tools. However, faced with the complexity of choosing the "right" assembly, meta-learning offers an attractive solution, learning from the problems of the past.  The algorithm selection problem is one of its applications [5]: given a dataset, identify which learning algorithm (and which hyperparameter setting) performs best on it.\\
  Algorithm Portfolio generalizes the problem and automates the construction of selection models [8]. The immediate goal is the same: to predict the results of the algorithms on a given problem without executing them. Even if, in the portfolio, some selection models are built by meta-learning [1], the purpose is different. The portfolio is based on the systematic acquisition of knowledge about the algorithms it contains. The research then focuses on the quality and the return of knowledge, the acquisition process itself and the construction of selection models over time.
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 ===== Objectives =====
- The construction of a portfolio requires covering a space of experiments broad enough to "cover" all the problems that may be submitted to it.
+ The construction of a portfolio requires covering a space of experiments broad enough to "cover" all the problems that may be submitted to it. \\
-However, (1) the space of problems and solutions presents a very great ((The variability subjects are related to pretreatment, algorithms, datasets, evaluation criteria, experimental results. Each subject has several variants. For example, in OpenML, for each dataset downloaded, 61 dataset meta-features are calculated[17]. There are more than a hundred classification algorithms[5], etc.)) “diversity” [16] even within a single class of problem like classification [9].
+However, (1) the space of problems and solutions presents a very great ((The variability subjects are related to pretreatment, algorithms, datasets, evaluation criteria, experimental results. Each subject has several variants. For example, in OpenML, for each dataset downloaded, 61 dataset meta-features are calculated[17]. There are more than a hundred classification algorithms[5], etc.)) “diversity” [16] even within a single class of problem like classification [9].\\
-(2) The resources required for ML experiments are massive (time, memory, energy)((The number of theoretical experiments to study p pretreatments, n algorithms and d data sets is 2^p*n*d. For 10 preprocessing algorithms, 100 classification algorithms and 100 sets of data, considering that each experiment only lasts one minute, it would take more than 7000 days of execution time.)).
+(2) The resources required for ML experiments are massive (time, memory, energy)((The number of theoretical experiments to study p pretreatments, n algorithms and d data sets is 2^p*n*d. For 10 preprocessing algorithms, 100 classification algorithms and 100 sets of data, considering that each experiment only lasts one minute, it would take more than 7000 days of execution time.)).  \\
-(3) As the ML domain is particularly productive, the portfolio must be able to evolve to integrate new algorithms.
+(3) As the ML domain is particularly productive, the portfolio must be able to evolve to integrate new algorithms.\\
 (4) To cope with the mass of data, the transformation of experimental results into knowledge requires the implementation of automatic analysis procedures.
-The objective of this thesis is, therefore, to propose different paradigms for constructing a portfolio of machine-learning workflows that meet these requirements: relevance of the results while taming the space of experiments, explanation of knowledge from meta-learning (the meta-learning is not a black box), automation of selection processes.
+The objective of this thesis is, therefore, to propose different paradigms for constructing a portfolio of machine-learning workflows that meet these requirements: relevance of the results while taming the space of experiments, explanation of knowledge from meta-learning (the meta-learning is not a black box), automation of selection processes. \\
 The PhD work will be organized to provide contributions in the following directions: \\
 - A representation of experiments in the form of graphs [10] and exploitation of these structures by adapted learning algorithms[3,15]; In particular, this approach should be exploited to reduce the search space and explain the choices made[4];\\