A meta-learning framework for pattern classification by means of data complexity measures

Sotoca, José Martínez; Mollineda, Ramón A.; Sánchez, J. Salvador

doi:10.4114/ia.v10i29.875

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…There are other descriptor-based approaches, for example for meta learning [8,9], for classifier recommendation [10], and for synthetic data generation [11]. However, only a small number of data points in a low dimensional space have been considered in these works.…”

Section: Descriptor-based Approachesmentioning

confidence: 99%

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Cho

Lee

2021

Applied Sciences

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Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

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Section: Descriptor-based Approachesmentioning

confidence: 99%

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Cho

Lee

2021

Applied Sciences

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Noisy-free Length Discriminant Analysis with cosine hyperbolic framework for dimensionality reduction

Murthy

Ghosh

2017

Expert Systems with Applications

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Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets

Olmo¹,

Romero²,

Gibaja³

et al. 2015

IJCIS

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Recommending classification algorithms is an open research problem the solution to which is of tremendous value for practitioners and non-experts data mining users such as educators. This paper proposes a new meta-learning framework for educational domains based on the use of multi-label learning for selecting the best classification algorithms in order to predict students' performance. In short, the framework considers an offline phase where statistical tests are performed to find the subset of algorithms that achieves the best performance over the repository of educational data sets. The subset of algorithms along with the meta-features extracted from the training data are used to generate a multi-label data set. A multi-label classifier is then trained and, in an online phase, this model is used to recommend the most suitable classification algorithms to be applied to new unseen data sets. This new multi-label metalearning approach has been applied to a repository of educational data sets generated from Moodle usage data. The results obtained show significant improvement compared with a previous nearest neighbor proposal, demonstrating the suitability of the new framework.

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A meta-learning framework for pattern classification by means of data complexity measures

Cited by 8 publications

References 15 publications

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Noisy-free Length Discriminant Analysis with cosine hyperbolic framework for dimensionality reduction

Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets

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