Feature Level Ensemble Method for Classifying Multi-media Data

Alyahyan, Saleh; Wang, Wenjia

doi:10.1007/978-3-319-71078-5_21

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Rules R0, R1 and R2 are described in our earlier work [11] [12]. We give a brief summary of them here for convenience as they are the bases of the new rule.…”

Section: The Decision Level Ensemble Methods Frameworkmentioning

confidence: 99%

“…The results of GDLEM were compared with the feature-level ensemble method(FLEM) and various heterogeneous ensembles based on the single media data, text (HEST) and image data (HESG). The full comparative results between the FLEM and the HESG were published in [12] and the full results for the HEST were published in [11]. Figure.…”

Section: Critical Comparison With Other Ensemblesmentioning

confidence: 99%

“…Following our previous studies [11] [12], this study presents a new generalised function for selecting classiers to build a heterogeneous ensemble based on two criteria -accuracy and diversity, combined with variable weights.…”

Section: Introductionmentioning

confidence: 99%

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Generalised Decision Level Ensemble Method for Classifying Multi-media Data

Alyahyan

Wang

2018

Lecture Notes in Computer Science

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In recent decades, multimedia data have been commonly generated and used in various domains, such as in healthcare and social media due to their ability of capturing rich information. But as they are unstructured and separated, how to fuse and integrate multimedia datasets and then learn from them eectively have been a main challenge to machine learning. We present a novel generalised decision level ensemble method (GDLEM) that combines the multimedia datasets at decision level. After extracting features from each of multimedia datasets separately, the method trains models independently on each media dataset and then employs a generalised selection function to choose the appropriate models to construct a heterogeneous ensemble. The selection function is dened as a weighted combination of two criteria: the accuracy of individual models and the diversity among the models. The framework is tested on multimedia data and compared with other heterogeneous ensembles. The results show that the GDLEM is more exible and eective.

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“…Rules R0, R1 and R2 are described in our earlier work [11] [12]. We give a brief summary of them here for convenience as they are the bases of the new rule.…”

Section: The Decision Level Ensemble Methods Frameworkmentioning

confidence: 99%

Section: Critical Comparison With Other Ensemblesmentioning

confidence: 99%

See 1 more Smart Citation

Generalised Decision Level Ensemble Method for Classifying Multi-media Data

Alyahyan

Wang

2018

Lecture Notes in Computer Science

Self Cite

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show abstract

“…The results were compared with the feature-level ensemble method (FLEM) and various heterogeneous ensembles based on the single media data, text (HEST) and image data (HESG). The full comparative results between the FLEM and the HESG were published in [2] and the full results for the HEST were published in [1]. Figure 9 shows the critical difference diagram for the DLEM, the FLEM, the HEST and the HESG, for all three rules R0, R1 and R2.…”

Section: Comparisonmentioning

confidence: 99%

“…A heterogeneous ensemble for classification combines multiple classifiers that are created by using different algorithms on different or same datasets, with the aim of making the classifiers more diverse and hence possibly increasing accuracy [8,15,18]. This work is a continuation of our previous research [1,2]. In this study we will investigate the problems of classifying multimedia data by combing them at decision level with heterogeneous ensembles.…”

Section: Introductionmentioning

confidence: 99%

Decision level ensemble method for classifying multi-media data

Alyahyan

Wang

2018

Wireless Netw

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In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining. In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly.

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MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm

Golshanrad

Rahmani

Karimian

et al. 2021

IDA

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Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.

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Feature Level Ensemble Method for Classifying Multi-media Data

Cited by 3 publications

References 19 publications

Generalised Decision Level Ensemble Method for Classifying Multi-media Data

Generalised Decision Level Ensemble Method for Classifying Multi-media Data

Decision level ensemble method for classifying multi-media data

MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm

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