Letter recognition using Holland-style adaptive classifiers

Frey, Peter W.; Slate, David J.

doi:10.1007/bf00114162

Cited by 257 publications

(159 citation statements)

References 14 publications

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“…The selected benchmark datasets include 3 small datasets: Glass, Iris [8] and Vehicle [37], as well as 5 prepartitioned larger datasets: Letter [11], Pendigits [2], Satimage, Segmentation and Thyroid [23]. The parameters of the used datasets are summarised in Table 1.…”

Section: Basic Conditionsmentioning

confidence: 99%

A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

Reiss

Hendeby

Stricker

2014

Pers Ubiquit Comput

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This paper addresses one of the main challenges in physical activity monitoring, as indicated by recent benchmark results: The difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. This algorithm is a variant of the AdaBoost.M1 that incorporates well-established ideas for confidence-based boosting. ConfAdaBoost.M1 is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository. Moreover, it is evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks. Finally, two empirical studies are designed and carried out to investigate the feasibility of ConfAdaBoost.M1 for physical activity monitoring applications in mobile systems

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Section: Basic Conditionsmentioning

confidence: 99%

A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

Reiss

Hendeby

Stricker

2014

Pers Ubiquit Comput

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“…The classification problems being addressed considered datasets from the Frey and Slate letter recognition problem [48]. The features which described the classification problems included the number of instances, number of classes, number of prototypes per class, number of relevant and irrelevant attributes, and the distribution range of the instances and prototypes.…”

Section: Algorithm Selection For Classification Problemsmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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“…6c indicates that organization has occurred in the vast region where outliers are located. On the basis of these images, the following set of outlying neurons is detected: (8, O), (9, O), (10, O), (11, O), (8,1), (9,1), (10,1), (11,1), (9,2), (10,2), (11,2), (10,3) and (11,3). Note that neurons (8,2), (9,3) and (11,4), pointed out by the MID image, have been exduded as the projection shows that they lie next to the main doud.…”

Section: Moderate Dispersionmentioning

confidence: 99%

“…There we find the 100 outliers plus seven other patterns sticking out at the right tail of the distribution. These seven extra patterns are the least outlying (in the box-plot) and project on to neurons (0,5), (0,7), (O, 11), (2, 1), (4,5), (6,1) and (7,11), none of which was labelled as outlying. These patterns may thus be seen as mild outliers projecting on to inlying neurons.…”

Section: Moderate Dispersionmentioning

confidence: 99%

Self-organizing maps for outlier detection

Muñoz

Muruzábal

1998

Neurocomputing

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In this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection ofthe trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data.Key Words Self-organization; Atypical Data; Robustness; Dimensionality Reduction; Nonlinear Projections .• Muñoz, Departamento de Estadística y Econometría, Universidad Carlos III de Madrid; Muruzábal, Departamento de Estadística y Econometría, Universidad Carlos III de Madrid. We are grateful to M. Botta and A. Atkinson for sharing their data sets. Support from CICYT and DGICYT (Spain) research grants is appreciated. Self-Organizing Maps for Outlier DetectionAlberto Muñoz and Jorge Muruzábal Department of Statistics and Econometrics U niversity Carlos III, 28903 Getafe, Spain e-mail: albmun@est-econ.uc3m.es.jorge@eco.uc3m.es AbstractIn this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection of the trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data.

show abstract

Letter recognition using Holland-style adaptive classifiers

Cited by 257 publications

References 14 publications

A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Self-organizing maps for outlier detection

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