scholarly journals Algorithm Selection: From Meta-Learning to Hyper-Heuristics

10.5772/36710 ◽  
2012 ◽  
Author(s):  
Laura Cruz-Reyes ◽  
Claudia Gmez-Santilln ◽  
Joaqun Prez-Ortega ◽  
Vanesa Landero ◽  
Marcela Quiroz ◽  
...  
Keyword(s):  
Algorithm Selection ◽  
Meta Learning

Meta-Learning for Periodic Algorithm Selection in Time-Changing Data

2012 ◽  
Author(s):  
Andre Luis Debiaso Rossi ◽  
Andre C.P.L.F. Carvalho ◽  
Carlos Soares
Keyword(s):  
Algorithm Selection ◽  
Meta Learning

Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection

2010 ◽  
pp. 36-51
Author(s):  
Lisa Fan ◽  
Minxiao Lei
Keyword(s):  
Data Mining ◽  
Model Building ◽  
Feature Reduction ◽  
Learning Method ◽  
Algorithm Selection ◽  
Data Mining Algorithms ◽  
Cognitive Overload ◽  
Meta Learning ◽  
Mining Model ◽  
Mining Algorithms

With the explosion of available data mining algorithms, a method for helping user to select the most appropriate algorithm or combination of algorithms to solve a given problem and reducing users’ cognitive overload due to the overloaded data mining algorithms is becoming increasingly important. This chapter presents a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The authors discuss the meta-learning method in detail and present some empirical results that show the improvement that can be achieved with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the dataset can be found. Thus, the ranking process can be sped up and accuracy can be increased by using the reduct of the properties of the dataset. With the reduced searching space, users’ cognitive load is reduced.

Meta-Learning

2011 ◽  
pp. 1207-1215 ◽  
Author(s):  
Christophe Giraud-Carrier ◽  
Pavel Brazdil ◽  
Carlos Soares ◽  
Ricardo Vilalta
Keyword(s):  
Data Mining ◽  
Credit Rating ◽  
Learning Task ◽  
End Users ◽  
Algorithm Selection ◽  
Local Optima ◽  
Base Level ◽  
Meta Learning ◽  
Automatic Mechanism ◽  
User Friendly

The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering). Unfortunately, due in part to the number of available techniques and the overall complexity of the process, users facing a new data mining task must generally either resort to trialand- error or consultation of experts. Clearly, neither solution is completely satisfactory for the non-expert end-users who wish to access the technology more directly and cost-effectively. What is needed is an informed search process to reduce the amount of experimentation with different techniques while avoiding the pitfalls of local optima that may result from low quality models. Informed search requires meta-knowledge, that is, knowledge about the performance of those techniques. Metalearning provides a robust, automatic mechanism for building such meta-knowledge. One of the underlying goals of meta-learning is to understand the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. Metalearning differs from base-level learning in the scope of adaptation. Whereas learning at the base-level focuses on accumulating experience on a specific learning task (e.g., credit rating, medical diagnosis, mine-rock discrimination, fraud detection, etc.), learning at the meta-level is concerned with accumulating experience on the performance of multiple applications of a learning system. The meta-knowledge induced by meta-learning provides the means to inform decisions about the precise conditions under which a given algorithm, or sequence of algorithms, is better than others for a given task. While Data Mining software packages (e.g., SAS Enterprise Miner, SPSS Clementine, Insightful Miner, PolyAnalyst, KnowledgeStudio, Weka, Yale, Xelopes) provide user-friendly access to rich collections of algorithms, they generally offer no real decision support to non-expert end-users. Similarly, tools with emphasis on advanced visualization help users understand the data (e.g., to select adequate transformations) and the models (e.g., to tweak parameters, compare results, and focus on specific parts of the model), but treat algorithm selection as a post-processing activity driven by the users rather than the system. Data mining practitioners need systems that guide them by producing explicit advice automatically. This chapter shows how meta-learning can be leveraged to provide such advice in the context of algorithm selection.

MetaStream: A meta-learning based method for periodic algorithm selection in time-changing data

2014 ◽  
Vol 127 ◽  
pp. 52-64 ◽  
Author(s):  
André Luis Debiaso Rossi ◽  
André Carlos Ponce de Leon Ferreira de Carvalho ◽  
Carlos Soares ◽  
Bruno Feres de Souza
Keyword(s):  
Algorithm Selection ◽  
Meta Learning

scholarly journals Framework of Intelligent System for Machine Learning Algorithm Selection in Social Sciences

2022 ◽  
pp. 21-28
Author(s):  
Dijana Oreški ◽  
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Data Set ◽  
Ranking Algorithms ◽  
Meta Learning ◽  
The Right

The ability to generate data has never been as powerful as today when three quintile bytes of data are generated daily. In the field of machine learning, a large number of algorithms have been developed, which can be used for intelligent data analysis and to solve prediction and descriptive problems in different domains. Developed algorithms have different effects on different problems.If one algorithmworks better on one dataset,the same algorithm may work worse on another data set. The reason is that each dataset has different features in terms of local and global characteristics. It is therefore imperative to know intrinsic algorithms behavior on different types of datasets andchoose the right algorithm for the problem solving. To address this problem, this papergives scientific contribution in meta learning field by proposing framework for identifying the specific characteristics of datasets in two domains of social sciences:education and business and develops meta models based on: ranking algorithms, calculating correlation of ranks, developing a multi-criteria model, two-component index and prediction based on machine learning algorithms. Each of the meta models serve as the basis for the development of intelligent system version. Application of such framework should include a comparative analysis of a large number of machine learning algorithms on a large number of datasetsfromsocial sciences.

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