Impact of Fuzziness Measures on the Performance of Semi-supervised Learning

Patwary, Muhammed J. A.; Wang, Xizhao; Yan, Dasen

doi:10.1007/s40815-019-00666-2

Cited by 24 publications

(3 citation statements)

References 46 publications

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“…Machine learning [10][11][12][13][14][15] techniques can be broadly categorized into two groups, such as supervised learning and unsupervised [16] learning. Supervised learning is the most popular approach, where the class label of each training tuple is known.…”

Section: Methodsmentioning

confidence: 99%

Bank Deposit Prediction Using Ensemble Learning

Patwary

Akter

Alam

et al. 2021

Artificial Intelligence Evolution

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Bank deposit is one of the vital issues for any financial institution. It is very challenging to predict a customer if he/she can be a depositor by analyzing related information. Some recent reports demonstrate that economic depression and the continuous decline of the economy negatively impact business organizations and banking sectors. Due to such economic depression, banks cannot attract a customer's attention. Thus, marketing is preferred to be a handy tool for the banking sector to draw customers' attention for a term deposit. The purpose of this paper is to study the performance of ensemble learning algorithms which is a novel approach to predict whether a new customer will have a term deposit or not. A Portuguese retail bank data is used for our study, containing 45,211 phone contacts with 16 input attributes and one decision attribute. The data are preprocessed by using the Discretization technique. 40,690 samples are used for training the classifiers, and 4,521 samples are used for testing. In this work, the performance of the three mostly used classification algorithms named Support Vector Machine (SVM), Neural Network (NN), and Naive Bayes (NB) are analyzed. Then the ability of ensemble methods to improve the efficiency of basic classification algorithms is investigated and experimentally demonstrated. Experimental results exhibit that the performance metrics of Neural Network (Bagging) is higher than other ensemble methods. Its accuracy, sensitivity, and specificity are 96.62%, 97.14%, and 99.08%, respectively. Although all input attributes are considered in the classification method, in the end, a descriptive analysis has shown that some input attributes have more importance for this classification. Overall, it is shown that ensemble methods outperformed the traditional algorithms in this domain. We believe our contribution can be used as a depositor prediction system to provide additional support for bank deposit prediction.

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Section: Methodsmentioning

confidence: 99%

Bank Deposit Prediction Using Ensemble Learning

Patwary

Akter

Alam

et al. 2021

Artificial Intelligence Evolution

Self Cite

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“…The term fuzziness was first proposed by Lotfi A. Zadeh in 1965 in association with his great invention fuzzy set theory [40]. And the theory has been used in semi-supervised learning [39], here we also use it to measure the prediction confidence of FWCDT.…”

Section: A Self-training Fwcdtmentioning

confidence: 99%

Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest

et al. 2020

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A self-training algorithm is an iterative method for semi-supervised learning, which wraps around a base learner. It uses its own predictions to assign labels to unlabeled data. For a self-training algorithm, the classification ability of the base learner and the estimation of prediction confidence are very important. The classical decision tree as the base learner cannot be effective in a self-training algorithm, because it cannot correctly estimate its own predictions. In this paper, we propose a novel method of node split of the decision trees, which uses weighted features to cluster instances. This method is able to combine multiple numerical and categorical features to split nodes. The decision tree and random forest constructed by this method are called FWCDT and FWCRF respectively. FWCDT and FWCRF have the better classification ability than the classical decision trees and forests based on univariate split when the training instances are fewer, therefore, they are more suitable as the base classifiers in self-training. What's more, on the basis of the proposed node-split method, we also respectively explore the suitable prediction confidence measurements for FWCDT and FWCRF. Finally, the results of experiment implemented on the UCI datasets show that the self-training feature weighted clustering decision tree (ST-FWCDT) and random forest (ST-FWCRF) can effectively exploit unlabeled data, and the final obtained classifiers have better generalization ability.

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“…Unimodal approach works on a single image, these are intergalactic time, stochastic, rule-based, & shape-based approaches. An intergalactic time approach describes the collection of temporal features [1,2] or trajectories [3,4] whereas stochastic approach comprise the action through statistical facsimiles [5][6][7] . The Rule-based approach follows custom protocol and designates human actions [8,9] .…”

Section: Introductionmentioning

confidence: 99%

Remarkable Skeleton Based Human Action Recognition

Jaiswal

2020

Artificial Intelligence Evolution

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Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance. However, the most challenging and crucial task of the skeleton-based human-action-recognition (SBHAR) is a significant view variation while capturing the data. In this area, a significant amount of satisfactory work has already been done, which include the Red Green Blue (RGB) data method. The performance of the SBHAR is also affected by the various factors such as video frame setting, view variations in motion, different backgrounds and inter-personal differences. In this survey, we explicitly address these challenges and provide a complete overview of advancement in this field. The deep learning method has been used in this field for a long time, but so far, no research has fully demonstrated its usefulness. In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e. NTU-RGB+D and its new version NTU-RGB+D 120.

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Impact of Fuzziness Measures on the Performance of Semi-supervised Learning

Cited by 24 publications

References 46 publications

Bank Deposit Prediction Using Ensemble Learning

Bank Deposit Prediction Using Ensemble Learning

Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest

Remarkable Skeleton Based Human Action Recognition

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