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Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity

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arXiv 2024年

作者： Barbudo, Rafael Ramírez, Aurora Romero, José Raúl Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain Córdoba Spain

The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow's unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow's specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workf

关键词： Machine learning

Modeling and Predicting Students’ Engagement Behaviors using Mixture Markov Models

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arXiv 2024年

作者： Maqsood, Rabia Ceravolo, Paolo Romero, Cristóbal Ventura, Sebastián Department of Computer Science National University of Computer and Emerging Sciences Pakistan Computer Science Department Universita degli Studi di Milano Milan Italy Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain

Students’ engagement reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for stimulating student engagement lie in the capability to have an approximate representation model for comprehending students’ varied (dis)engagement behaviors. In this paper, we utilized model-based clustering for this purpose which generates K mixture Markov models to group students’ traces containing their (dis)engagement behavioral patterns. To prevent the Expectation-Maximization (EM) algorithm from getting stuck in a local maxima, we also introduced a new initialization method named as K-EM. The proposed method initializes the EM algorithm using the results of a preliminary K-means clustering algorithm performed on students’ logged problem-solving actions. We performed an experimental work on two real datasets using the three variants of the EM algorithm: the original EM, emEM, K-EM;and, non-mixture baseline models for both datasets. The proposed K-EM method has shown very promising results and achieved significant performance difference in comparison to the other approaches particularly using the Dataset1 (which contains small length traces in contrast to the Dataset2). Hence, we suggest to perform further experiments using large dataset(s) to validate our method. Additionally, visualization of the resultant K mixtures (or clusters) through first-order Markov chains reveal very useful insights about (dis)engagement behaviors depicted by the students. We conclude the paper with a discussion on the usefulness of our approach, limitations and potential extensions of this work. Copyright © 2024, The Authors. All rights reserved.

关键词： K-means clustering

Pole Identification using discrete Laguerre expansion and variable projection

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IFAC-PapersOnLine 2023年第2期56卷 3994-3999页

作者： Tamás Dózsa Mátyás Szabari Alexandros Soumelidis Péter Kovács Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis Eötvös Loránd University Faculty of Science Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis

We propose a novel algorithm for identifying the poles of transfer functions describing SISO-LTI (single input single output, linear time invariant) systems. Our Identification method works in the frequency domain and consists of two parts. In the first part, we extend a discrete Laguerre expansion based method with an automatic parameter selection scheme. This allows us to find an initial estimate of the poles of SISO-LTI transfer functions without the need for human intuition. Then, in the second part, we propose a novel optimization problem to improve our initial estimates. The proposed optimization aims to reduce the least squared error of a parameterized model, which can be interpreted as an orthogonal projection of the system's frequency response onto a subspace spanned by Generalized Orthogonal Rational Basis functions (GOBFs). We solve the corresponding nonlinear optimization task using gradient based methods, where we can analytically calculate the gradient of the error functional. Through robust numerical experiments, we investigate the behavior of the developed methods and show that they work even in scenarios, when the transfer function has a high number of poles.

关键词： Frequency domain identification Recursive identification Linear systems Variable Projection Generalized Orthogonal Basis Functions, Malmquist-Takenaka functions

HIGH CURVATURE MEANS LOW-RANK: ON THE SECTIONAL CURVATURE OF GRASSMANN AND STIEFEL MANIFOLDS AND THE UNDERLYING MATRIX TRACE INEQUALITIES

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arXiv 2024年

作者： Zimmermann, Ralf Stoye, Jakob University of Southern Denmark Department of Mathematics and Computer Science Odense Denmark TU Braunschweig Institute for Numerical Analysis Braunschweig Germany

Methods and algorithms that work with data on nonlinear manifolds are collectively summarized under the term ‘Riemannian computing’. In practice, curvature can be a key limiting factor for the performance of Riemannian computing methods. Yet, curvature can also be a powerful tool in the theoretical analysis of Riemannian algorithms. In this work, we investigate the sectional curvature of the Stiefel and Grassmann manifold. On the Grassmannian, tight curvature bounds are known since the late 1960ies. On the Stiefel manifold under the canonical metric, it was believed that the sectional curvature does not exceed 5/4. Under the Euclidean metric, the maximum was conjectured to be at 1. For both manifolds, the sectional curvature is given by the Frobenius norm of certain structured commutator brackets of skew-symmetric matrices. We provide refined inequalities for such terms and pay special attention to the maximizers of the curvature bounds. In this way, we prove for the Stiefel manifold that the global bounds of 5/4 (canonical metric) and 1 (Euclidean metric) hold indeed. With this addition, a complete account of the curvature bounds in all admissible dimensions is obtained. We observe that ‘high curvature means low-rank’, more precisely, for the Stiefel and Grassmann manifolds under the canonical metric, the global curvature maximum is attained at tangent plane sections that are spanned by rank-two matrices, while the extreme curvature cases of the Euclidean Stiefel manifold occur for rank-one matrices. numerical examples are included for illustration *** Codes 15B10, 15B57, 15B30, 65F99, 22E70, 53C30, 53C80 © 2024, CC BY-NC-SA.

关键词： Matrix algebra

GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection

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arXiv 2024年

作者： Barbudo, Rafael Ramírez, Aurora Servant, Francisco Romero, José Raúl Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain Department of Computer Science Virginia Tech BlacksburgVA24060 United States

Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern Finally a demonstration tool is also *** Codes 68W50 © 2024, CC BY.

关键词： Machine learning

Studying the Effect of Different Lp Norms in the Context of Time Series Ordinal Classification 19th

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Studying the Effect of Different Lp Norms in the Context of ...

19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020/2021

作者： Guijo-Rubio, David Vargas, Víctor Manuel Gutiérrez, Pedro Antonio Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain

ISBN: (纸本)9783030857127

Time Series Ordinal Classification (TSOC) is yet an unexplored field of machine learning consisting in the classification of time series whose labels follow a natural order relationship between them. In this context, a well-known approach for time series nominal classification was previously used: the Shapelet Transform (ST). The exploitation of the ordinal information was included in two steps of the ST algorithm: 1) by using the Pearson’s determination coefficient (R2 ) for computing the quality of the shapelets, which favours shapelets with better ordering, and 2) by applying an ordinal classifier instead of a nominal one to the transformed dataset. For this, the distance between labels was represented by the absolute value of the difference between the corresponding ranks, i.e. by the L1 norm. In this paper, we study the behaviour of different Lp norms for representing class distances in ordinal regression, evaluating 9 different Lp norms with 7 ordinal time series datasets from the UEA-UCR time series classification repository and 10 different ordinal classifiers. The results achieved demonstrate that the Pearson’s determination coefficient using the L1.9 norm in the computation of the difference between the shapelet and the time series labels achieves a significantly better performance when compared to the rest of the approaches, in terms of both Correct Classification Rate (CCR) and Average Mean Absolute Error (AMAE). © 2021, Springer Nature Switzerland AG.

关键词： Time series

Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions

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arXiv 2024年

作者： Vargas, Víctor Manuel Gutiérrez, Pedro Antonio Gómez, Javier Barbero Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Albert Einstein building Córdoba14014 Spain

An ordinal classification problem is one in which the target variable takes values on an ordinal scale. Nowadays, there are many of these problems associated with real-world tasks where it is crucial to accurately classify the extreme classes of the ordinal structure. In this work, we propose a unimodal regularisation approach that can be applied to any loss function to improve the classification performance of the first and last classes while maintaining good performance for the remainder. The proposed methodology is tested on six datasets with different numbers of classes, and compared with other unimodal regularisation methods in the literature. In addition, performance in the extreme classes is compared using a new metric that takes into account their sensitivities. Experimental results and statistical analysis show that the proposed methodology obtains a superior average performance considering different metrics. The results for the proposed metric show that the generalised beta distribution generally improves classification performance in the extreme classes. At the same time, the other five nominal and ordinal metrics considered show that the overall performance is aligned with the performance of previous alternatives. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners

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arXiv 2024年

作者： Luna, J.M. Fardoun, H.M. Padillo, F. Romero, C. Ventura, S. Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope with extremely large datasets. As an additional feature, the proposal includes a threshold value to denote the number of courses that each discovered rule should satisfy. A post-processing step is also included so redundant subgroups can be removed. The experimental stage is carried out by considering de-identified data from the first year of 16 MITx and HarvardX courses on the edX platform. Experimental results demonstrate that the proposed MapReduce approach outperforms traditional sequential subgroup discovery approaches, achieving a runtime that is almost constant for different courses. Additionally, thanks to the final post-processing step, only interesting and not-redundant rules are discovered, hence reducing the number of subgroups in one or two orders of magnitude. Finally, the discovered subgroups are easily used by courses’ instructors not only for descriptive purposes but also for additional tasks such as recommendation or personalization. Copyright © 2024, The Authors. All rights reserved.

关键词： MapReduce

Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

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arXiv 2024年

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. © 2024, CC BY.

关键词： Generative adversarial networks