Ensemble Decision Making System for Breast Cancer Data

Lavanya, D.; Rani, K. Usha

doi:10.5120/8134-1823

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Moreover, tree‐based methods have proved useful for building recommender systems in different areas outperforming other approaches (Utku et al, 2015). However, the accuracy of prediction may suffer from both the size of the available data set (Bar et al, 2013; Ghimire et al, 2012) and the number of features (Lavanya & Rani, 2012). Our previous studies have demonstrated the superior performance of tree‐based ensembles relative to single decision‐trees, as well as their dependency on the number of available features (Almomani et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Application of choice models in tourism recommender systems

et al. 2022

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Choice models (CM) are proposed in the field of tourism recommender systems (TRS) with the aim of providing algorithms with both a theoretical understanding of tourist's motivations and a certain degree of transparency. The goal of this work is to overcome some of the limitations of current state-of-art algorithms used in TRSs by providing: (1) accurate preferences, which are learnt from user choices rather than from ratings, and (2) interpretable coefficients, which are achieved by means of the set of estimated parameters of CM. The study was carried out with a gastronomic data set generated in an ecological experiment in the tourism domain. The performance of CM has been compared with a set of baseline algorithms (rating-based and ensembles) by using two evaluation metrics: precision and DCG. The CM outperformed the baseline algorithms when the size of the choice set was limited. The findings suggest that CM may provide an optimal trade-off between theoretical soundness, interpretability and performance in the field of TRS.

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Section: Methodsmentioning

confidence: 99%

Application of choice models in tourism recommender systems

et al. 2022

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“…The performance of ensemble models in medical diagnosis has not been extensively studied, with the exception of [27] and [15], which show that bagging and boosting learning perform better than a single model. The fact that the medical dataset is typically class-imbalanced is yet another factor that can make things more complicated.…”

Section: Related Workmentioning

confidence: 99%

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Nguyen

2023

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In recent years, the increasing volume and availability of healthcare and biomedical data are opening up new opportunities for computational methods to enhance healthcare in many hospitals. Medical data classification is regarded as the challenging task to develop intelligent medical decision support systems in hospitals. In this paper, the ensemble approaches based on support vector machines are proposed for classifying medical data. This research’s key contribution is that the ensemble multiple support vector machines use the function kernel in the style of gradient boosting and bagging to produce a more accurate fusion model than the mono-modality models. Extensive experiments have been conducted on forty benchmark medical datasets from the University of California at Irvine machine learning repository. The classification results show that there is a statistically significant difference (p-values < 0.05) between the proposed approaches and the best classification models. In addition, the empirical analysis of forty medical datasets indicated that our models can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.

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“…The results for the best implementation of a SVM put the classification accuracy at about 96.9957%. In Lavanya and Rani [38], the classification and regression trees (CART) method was tested on the Breast Cancer (Original) data set. This method uses recursive partitioning in a regression tree algorithm hybridized with bagging, a bootstrap aggregation method.…”

Section: Observations On Breast Cancermentioning

confidence: 99%

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Pickens

Sengupta

2021

MAKE

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Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems.

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Ensemble Decision Making System for Breast Cancer Data

Cited by 11 publications

References 19 publications

Application of choice models in tourism recommender systems

Application of choice models in tourism recommender systems

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

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