Dynamic selection of the best base classifier in One versus One

Mendialdua, Iñigo; Martínez-Otzeta, José María; Rodriguez, Igor; Ruiz-Vazquez, Txelo; Sierra, Basilio

doi:10.1016/j.knosys.2015.05.015

Cited by 24 publications

(12 citation statements)

References 31 publications

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“…Once the data have been processed, the previously explained CSP algorithm is performed. The used CSP method is implemented to work with just two classes, therefore all the tests have been carried out using pairs of classes, although multiclass classification is possible using pairwise classification approaches, such as One versus One (OVO) as a class binarization technique [50].…”

Section: Resultsmentioning

confidence: 99%

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Rodríguez-Moreno

Martínez-Otzeta

Goienetxea

et al. 2020

Sensors

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Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a more natural social interaction. The application of Common Spatial Patterns (CSP), a signal processing approach widely used in electroencephalography (EEG), is presented in a novel manner to be used in activity recognition in videos taken by a humanoid robot. A sequence of skeleton data is considered as a multidimensional signal and filtered according to the CSP algorithm. Then, characteristics extracted from these filtered data are used as features for a classifier. A database with 46 individuals performing six different actions has been created to test the proposed method. The CSP-based method along with a Linear Discriminant Analysis (LDA) classifier has been compared to a Long Short-Term Memory (LSTM) neural network, showing that the former obtains similar or better results than the latter, while being simpler.

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Section: Resultsmentioning

confidence: 99%

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Rodríguez-Moreno

Martínez-Otzeta

Goienetxea

et al. 2020

Sensors

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“…In this regard, ensemble learning approach consisting of three classifiers including Support vector machine(SVM), Artificial neural network(ANN) and Gradient boosting Decision trees(XGBoost) has been used where an output score is produced according to ensemble classifier rule based on input scores of all three classifiers as shown in Fig. 9 The proposed system employs two types of ensemble rules including dynamic classifier selection(DCS) [38] and weighted classifier fusion(WCF) [39]. DCS reflects the tendency to extract a single best classifier at train-test split for each action which is the most likely to produce the correct classification label for an input sample at validation split.…”

Section: System Architecturementioning

confidence: 99%

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

et al. 2020

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User authentication is considered to be an important aspect of any cyber security program. However, one-time validation of user's identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented which tends to authenticate the user on each action. The proposed 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model, decides the genuineness of user based on each action and locks out the imposter user based on final threshold, however, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.

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“…These methods are based on the classification accuracy of the local neighborhood of the test sample, where the neighborhood is defined by the k nearest neighbors (KNN) algorithm [15] or the clustering algorithm [16]. For example, the overall local accuracy (OLA) selects the optimal classifier based on the accuracy of the classifier in the local neighborhood [17]. Another method is the local class accuracy (LCA), which uses posteriori information to calculate the performance of the base classifier for particular classes [18].…”

Section: Classification Accuracy Based On Local Neighborhoodmentioning

confidence: 99%

Sample awareness-based personalized facial expression recognition

Wen

2019

Appl Intell

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The behavior of the current emotion classification model to recognize all test samples using the same method contradicts the cognition of human beings in the real world, who dynamically change the methods they use based on current test samples. To address this contradiction, this study proposes an individualized emotion recognition method based on context awareness. For a given test sample, a classifier that was deemed the most suitable for the current test sample was first selected from a set of candidate classifiers and then used to realize the individualized emotion recognition. The Bayesian learning method was applied to select the optimal classifier and then evaluate each candidate classifier from the global perspective to guarantee the optimality of each candidate classifier. The results of the study validated the effectiveness of the proposed method.

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Dynamic selection of the best base classifier in One versus One

Cited by 24 publications

References 31 publications

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach

Sample awareness-based personalized facial expression recognition

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