Decision level ensemble method for classifying multi-media data

Alyahyan, Saleh; Wang, Wenjia

doi:10.1007/s11276-018-01906-3

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Moreover, most of the existing works risk overfitting or underfitting due to high bias and variance on complex and relatively smaller datasets, potentially leading to suboptimal generalization and sensitivity to noise. Conversely, ensemble learning techniques, employed to mitigate bias and variance while enhancing model performance, have been applied at both the feature level and decision level [8]. Ensemble is a technique where heterogeneous or homogeneous model characteristics are combined to enhance the overall performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Infusing Weighted Average Ensemble Diversity for Advanced Breast Cancer Detection

Abhisheka,

Biswas,

Purkayastha

2024

Int J Imaging Syst Tech

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Breast cancer is a widespread health threat for women globally, often difficult to detect early due to its asymptomatic nature. As the disease advances, treatment becomes intricate and costly, ultimately resulting in elevated fatality rates. Currently, despite the widespread use of advanced machine learning (ML) and deep learning (DL) techniques, a comprehensive diagnosis of breast cancer remains elusive. Most of the existing methods primarily utilize either attention‐based deep models or models based on handcrafted features to capture and gather local details. However, both of these approaches lack the capability to offer essential local information for precise tumor detection. Additionally, the available breast cancer datasets suffer from class imbalance issue. Hence, this paper presents a novel weighted average ensemble network (WA‐ENet) designed for early‐stage breast cancer detection that leverages the ability of ensemble technique over single classifier‐based models for more robust and accurate prediction. The proposed model employs a weighted average‐based ensemble technique, combining predictions from three diverse classifiers. The optimal combination of weights is determined using the hill climbing (HC) algorithm. Moreover, the proposed model enhances overall system performance by integrating deep features and handcrafted features through the use of HOG, thereby providing precise local information. Additionally, the proposed work addresses class imbalance by incorporating borderline synthetic minority over‐sampling technique (BSMOTE). It achieves 99.65% accuracy on BUSI and 97.48% on UDIAT datasets.

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Section: Introductionmentioning

confidence: 99%

Infusing Weighted Average Ensemble Diversity for Advanced Breast Cancer Detection

Abhisheka,

Biswas,

Purkayastha

2024

Int J Imaging Syst Tech

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“…This broad research area has been named in different ways, for instance, sensor data fusion, decision fusion, multimodal fusion, heterogeneous sensor fusion, mixture of experts, classifier combination, and multiway signal processing. The applications of fusion methods cover a large number of interesting problems including emotion recognition, fall detection, daily activity recognition, and hand movement recognition [1]; social networks [2]; Alzheimer's disease [3]; automatic sleep staging [4]; image object classification [5]; multi-media [6]; brain computer interfacing [7]; archaeological ceramic provenance [8]; object detection fusing infrared and visible images [9]; banking customer classification [10]; stock movement prediction [11]; maritime tracking [12]; and several benchmark datasets [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Graph Regularization Methods in Soft Detector Fusion

Salazar,

Safont,

Vergara

et al. 2023

IEEE Access

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This paper presents a theoretical derivation of two new graph-based regularization methods for fusing the individual results of multiple detectors (two-class classifiers). The proposed approach considers linear combination of the individual detector statistics and its extension to a general nonlinear fusion method known as α-integration. A cost function that includes a mean-square error and a regularization term is minimized. The inclusion of the regularization term, which is based on graph signal processing, reduces the dispersion of the fused statistics, and thus improves the separation between the fused statistics corresponding to every detection hypothesis. The proposed methods (linear and non-linear regularized α-integration) are experimentally compared with commonly used classification methods (random forest, linear and quadratic discriminant analysis, and naive Bayes) and competitive fusion methods (Dempster-Shafer, copulas, behavior knowledge space, independent component analysis mixture modeling, majority voting, the mean, and αintegration). Two challenging problems were approached using simulated and electroencephalographic data, respectively: (i) detection of ultrasound pulses buried in high noise, and (ii) detection of changes in electroencephalographic signals for neuropsychological test staging. An experimental convergence analysis of the proposed regularized method for these two applications is included. Besides, the proposed methods were tested using several benchmark datasets. Results on the basis of classification accuracy, kappa index, F1 score, and receiver operating characteristic curve analysis demonstrate the superiority of the proposed regularized fusion methods.

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“…The authors in [12] develop an innovative machine learning heterogeneous ensemble algorithm to enhance the accuracy and reliability of classifying Multi-media Data (MMD) containing several different types of data such as numbers, text and images. The method they have developed consists of four consecutive stages: (1) features are extracted from each media data sub-set, then (2), modelling is performed independently on each of these datasets using a variety of base learning algorithms, (3) models are selected according to either their accuracy alone, or both their accuracy and diversity, and (4) an ensemble combines the outcomes from the selected models at the decision level.…”

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confidence: 99%