Feature Selection Inspired Classifier Ensemble Reduction

Diao, Ren; Chao, Fei; Peng, Taoxin; Snooke, Neal; Shen, Qiang

doi:10.1109/tcyb.2013.2281820

Cited by 89 publications

(51 citation statements)

References 49 publications

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“…Therefore, future work can be supported by involving heuristic search instead of sequential search selection. If the work is extended involving ensemble [7] and embedded [2] methods; it may support with clear insight in complexity analysis and direction for further improvements.…”

Section: Resultsmentioning

confidence: 98%

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Kale¹,

Sonavane²

2017

Data-Enabled Discov. Appl.

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Extreme learning machine (ELM) is a fast learning algorithm which has various advancements in terms of speed, reliability and generalized performance. Perhaps, it lacks the ability to solve the weighted classification problem. One of the alternative solutions to this problem is fuzzy-ELM (F-ELM) which combines the advantages of a fuzzy system and ELM. The major aim of this research is to handle the feature subset selection (FSS) and weighted classification problem. In this paper, a hybrid FSS approach for F-ELM classifier (H-FELM) is designed for binary and multiclass classification on the clinical dataset. Experimental results illustrate that H-FELM has the capability to handle these problems with improved classification accuracy by selecting prominent features which are relevant and nonredundant. The selection of such features can reduce the computational overhead and hence minimize the learning time. In order to validate the efficiency and effectiveness of the proposed algorithm, the comparative performance is evaluated by using four different strategies-1. existing F-ELM, 2. existing F-ELM with FSS, 3. ELM and F-ELM and 4. ELM with FSS and F-ELM with FSS. It is observed that the designed algorithm achieves an improvement of 9-10%

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Section: Resultsmentioning

confidence: 98%

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Kale¹,

Sonavane²

2017

Data-Enabled Discov. Appl.

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“…Building on previous work by Adama et al (2018) in which a selection of learning models was used separately to identify activities, this work employs a combination of different learning models in a framework referred to as a bagging ensemble of classifiers in order to achieve an improved performance of the system. The use of an ensemble of classifiers model generally allows for better predictive performance than the performance achievable with a single model (Diao et al 2014;Yao et al 2016). According to Tahir et al (2012), ensemble models are learning models that construct a set of classifiers used in classifying new information based on a weighted vote of individual classifier predictions.…”

Section: Classifier Ensemble Modelmentioning

confidence: 99%

Human activity learning for assistive robotics using a classifier ensemble

et al. 2018

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Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers-support vector machines (SVMs), K -nearest neighbour and random forest-is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset-the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

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“…However, empirical results have shown that in certain situations, dependent weights do not always perform as expected. Besides, retaining more diversity of base members in the aggregated output is sometimes preferable [25], [26]. Inspired by these observations, and in order to generalise the dependent determination of the weighting vectors in kNN-DOWA, the k-Nearest-Neighbour-Induced OWA is herein proposed.…”

Section: B Induced Owa Aggregationmentioning

confidence: 99%

Nearest neighbour-guided induced OWA and its application to journal ranking

Chen

Shang

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Aggregation operators are useful tools which summarise multiple inputs to a single output. In practice, inputs to such operators are variables which represent different criteria, measurements, or opinions from experts. In this paper, a nearest neighbour-guided induced OWA operator, abbreviated as kNN-IOWA, is proposed as a special case of the generic induced OWA where the input arguments are ordered by the average distances to their k nearest neighbours. The weighting vectors in kNN-IOWA are defined, which are used to interpret the overall behaviour of the operator's reliability. kNN-IOWA is applied for building aggregated fuzzy relations between academic journals, based on their indicator scores. It combines the similarities between academic journals to assess their performance with respect to different journal impact indicators. The work is compared against different types of aggregation operator and tested on six bibliometric datasets. The results of experimental evaluation demonstrate that kNN-IOWA outperforms other aggregation operators in terms of standard accuracy and within-1 accuracy. The proposed method also exhibits the advantages of being more intuitive and interpretable.

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Feature Selection Inspired Classifier Ensemble Reduction

Cited by 89 publications

References 49 publications

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Hybrid Feature Subset Selection Approach for Fuzzy-Extreme Learning Machine

Human activity learning for assistive robotics using a classifier ensemble

Nearest neighbour-guided induced OWA and its application to journal ranking

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