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Kuncheva, Ludmila I.; Whitaker, Christopher J.

doi:10.1023/a:1022859003006

Cited by 1,889 publications

(199 citation statements)

References 31 publications

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“…These data were combined and seven different data sets were generated. (16)(17)(18) Mean, variance, standard deviation, skewness, and kurtosis were extracted as five time-domain features, and energy and entropy were extracted as two frequency-domain features. (19,20) These selected features were used as original input features for the model algorithm and selected once again in the internal RF algorithm.…”

Section: Rf Algorithmmentioning

confidence: 99%

Online Human Daily Activity Recognition with Rechargeable Wearable Sensors

2017

Sensors and Materials

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This paper describes an online human physical activity (PA) recognition system based on machine learning, using rechargeable wireless wearable sensors with body-energy harvesting. The entire system is introduced and described, including the wireless wearable sensor network with a control center (smartphone), a body-energy harvesting module as the power supply for the sensors, and a PA recognition model based on the random forest algorithm. Hardware design, software design, and algorithm design are described in detail. For the hardware design, there are two main parts: the low-power wireless wearable sensors using Bluetooth 4.0 BLE, and the energy harvesting module. Two experiments were carried out to validate the design. One was an energy harvesting and consumption experiment on the wearable sensors, which determined that harvesting human body energy to supply the wearable sensors is feasible. The other was online human PA recognition based on the rechargeable wearable sensors. According to the results, the body-energy harvesting module has almost no influence on the recognition accuracy, and using the human body movement energy harvesting system for online human PA recognition is practical.

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Section: Rf Algorithmmentioning

confidence: 99%

Online Human Daily Activity Recognition with Rechargeable Wearable Sensors

2017

Sensors and Materials

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“…Therefore, we should be able to solve our new challenge without the need to build a totally new model. The idea of mixing many different models is very old in machine learning literature (Rasmussen & Ghahramani 1991;Jordan & Jacobs 1993;Meir 1996;Breiman 2001;Kuncheva & Whitaker 2003;Kuncheva 2007;Bishop & Svensen 2012;Chamroukhi 2015). These approaches are guided by the "divide and conquer" principle, in which each expert focuses on a particular area of feature space.…”

Section: A Model That Classifies Rr Lyraementioning

confidence: 99%

“…The idea of studying model outputs has been used before, but in different contexts, such as anomaly detection (Nun et al 2014) and measurements of diversity (Kuncheva & Whitaker 2003).…”

Section: A Model That Classifies Rr Lyraementioning

confidence: 99%

Meta-Classification for Variable Stars

Pichara

Protopapas

León

2016

ApJ

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The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New scientific problems emerge, and it is critical to be able to reuse the models learned before, without rebuilding everything from the beginning when the sciencientific problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. A conventional mixture of expert algorithms in machine learning literature cannot be used since each expert (model) uses different inputs. We also consider the computational complexity of the model by using the most expensive models only when it is necessary. We test our model with EROS-2 and MACHO data sets, and we show that we solve most of the classification challenges only by training a meta-model to learn how to integrate the previous experts.

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“…A number of diversity measures have been proposed over the years [1,2,3]. Most measures have been derived intuitively, as attempts to formally characterize the pattern of error of individual classifiers (e.g., the Double-Fault and Disagreement measures [2]).…”

Section: Introductionmentioning

confidence: 99%

“…A few other measures have been inspired by exact error decompositions derived in the regression field, despite the lack of a direct analogy to classification problems [5]. The Kohavi-Wolpert Variance [3] (and our attempt in [6]) was inspired by the biasvariance-covariance error decomposition of [7]. The measure derived in [8] (which we extended in [6]) was inspired by the ambiguity decomposition of [9], and provided useful insights, leading to the concept of 'good' and 'bad' patterns of diversity.…”

Section: Introductionmentioning

confidence: 99%

Using Diversity for Classifier Ensemble Pruning: An Empirical Investigation

Ahmed¹,

Didaci²,

Lavi³

et al. 2018

Theor. Appl. Inform.

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The concept of 'diversity' has been one of the main open issues in the field of multiple classifier systems. In this paper we address a facet of diversity related to its effectiveness for ensemble construction, namely, explicitly using diversity measures for ensemble construction techniques based on the kind of overproduce and choose strategy known as ensemble pruning. Such a strategy consists of selecting the (hopefully) more accurate subset of classifiers out of an original, larger ensemble. Whereas several existing pruning methods use some combination of individual classifiers' accuracy and diversity, it is still unclear whether such an evaluation function is better than the bare estimate of ensemble accuracy. We empirically investigate this issue by comparing two evaluation functions in the context of ensemble pruning: the estimate of ensemble accuracy, and its linear combination with several well-known diversity measures. This can also be viewed as using diversity as a regularizer, as suggested by some authors. To this aim we use a pruning method based on forward selection, since it allows a direct comparison between different evaluation functions. Experiments on thirty-seven benchmark data sets, four diversity measures and three base classifiers provide evidence that using diversity measures for ensemble pruning can be advantageous over using only ensemble accuracy, and that diversity measures can act as regularizers in this context.

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Cited by 1,889 publications

References 31 publications

Online Human Daily Activity Recognition with Rechargeable Wearable Sensors

Online Human Daily Activity Recognition with Rechargeable Wearable Sensors

Meta-Classification for Variable Stars

Using Diversity for Classifier Ensemble Pruning: An Empirical Investigation

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