A new ensemble learning method based on learning automata

Savargiv, Mohammad; Masoumi, Behrooz; Keyvanpour, Mohammad Reza

doi:10.1007/s12652-020-01882-7

Cited by 23 publications

(9 citation statements)

References 51 publications

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“…These algorithms thoroughly explore the search space and easily return the global optimal condition. The TSA 31 is a new bio‐inspired metaheuristic optimization algorithm introduced to optimize nonlinear issues. This algorithm is stimulated with swarming nature of the tunicate to successfully live on depths of the ocean.…”

Section: Proposed Methodsmentioning

confidence: 99%

Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

Srikanth

Huq

Kumar

2021

Concurrency and Computation

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Breast cancer is a very dangerous disease that mainly affects women. It is a deadliest disease that highly affects the women's life. Therefore, it is necessary to predict and classify this deadly disease for early diagnosis. There exist numerous data mining techniques for early prediction and classification of this disease. The big data based analytical model provides the better solution for storing, manipulating, and analyzing a great number of mammographic images. In this article, a new improved fractional rough fuzzy K-means clustering strategy is considered for disease prediction. Then, a new Tunicate Swarm Algorithm (TSA) is introduced to optimize the weight parameters.TSA is a bio-inspired metaheuristic optimization approach. Finally, the labeled ensemble classifier (LEC) is utilized for classifying the stages of breast cancer as malignant and benign. Here, the data is randomly generated from breast cancer Wisconsin dataset (diagnosis) obtainable on UCI machine learning repository. The proposed strategy is compared with different existing strategies, like Logistic Regression Classifier, Random Forest Classifier. From the analysis, it is observed that the proposed big data based analytical model using LEC provides 99.3% accuracy that is very high when compared to the accuracy of existing approaches.

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Section: Proposed Methodsmentioning

confidence: 99%

Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

Srikanth

Huq

Kumar

2021

Concurrency and Computation

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“…The OOV word is defined as unorthodox words, and it has not been presented in the standard dictionary for reference. To tackle this issue, many research works had been carried out [26][27][28][29][30] and attained some considerable accuracy rates with respect to the BLEU score. The researchers [31] have used the classifier to detect the OOV words as ill-formed words based on the similarity measures such as phoneme and grapheme score and converted the ill-formed words into standard English words.…”

Section: Related Workmentioning

confidence: 99%

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

Narayanasamy

Qaisar

et al. 2022

Journal of Sensors

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The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.

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“…The learning capacity of label ranking is utilized in many applications 13–15 . The ensemble technique improves the accuracy of prediction by combining the predictive power of different algorithms 16 . This methodology is worked on complete label ranking datasets but some recent studies mainly focused on partial label ranking algorithms 17,18 .…”

Section: Literature Reviewmentioning

confidence: 99%

Label ranking‐based recommendation system to rank crops for agroecological units

Suchithra

Pai

2021

Concurrency and Computation

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Data mining and machine learning techniques help to predict suitable crops in ranking order for a given location with its soil nutrient status. We proposed a tuned bagging‐based K‐nearest neighbor ensemble label ranker and it is used to predict the ranked crops for density‐based spatial clustering of applications with noise (DBSCAN) based compressed crop‐ranked agricultural data. This ensemble integrates the commonly used Borda rank aggregation method, but there is a possibility to improve the performance of our proposed algorithm by selecting the most suitable rank aggregation based on the improvement in the label ranked dataset. The results of the study show that Copeland performed better than Borda aggregation with a 6.65% improvement in the performance of our algorithm, while it is 5.16% for Borda. The voting rule selection (VRS) is also integrated into our study with dataset‐level and instance‐level learning on crop‐ranked datasets. VRS and Copeland aggregation methods shared the first rank position with 46.14% and 45.67% improvement in the ranked datasets. The instance‐level winning percentage of crop ranked data for VRS, Copeland, and Borda is 61.44%, 56.23%, and 47.81%, respectively. This study has observed that in the case of a tuned K‐nearest neighbor label ranking algorithm, the VRS and Copeland voting rule performs better than Borda.

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A new ensemble learning method based on learning automata

Cited by 23 publications

References 51 publications

Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

Label ranking‐based recommendation system to rank crops for agroecological units

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