On the Classification of Imbalanced Datasets

N, Arun Kumar M; Sheshadri, H. S.

doi:10.5120/6280-8449

Cited by 23 publications

(18 citation statements)

References 42 publications

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“…The problem of class imbalancing (i.e. number of feature points from one class is significantly higher than that of the other one) is regarded a crucial problem in machine learning literature and therefore many approaches exist to solve it [24] in order to produce a balanced classifier for the subsequent layers. Popular solutions are sampling techniques like sub-or oversampling additionally combined with boosting.…”

Section: • Compensation Of Missing Data Points Should Be Includedmentioning

confidence: 99%

Cascaded Fusion of Dynamic, Spatial, and Textural Feature Sets for Person-Independent Facial Emotion Recognition

Kächele¹,

Schwenker

2014

2014 22nd International Conference on Pattern Recognition

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Emotion recognition from facial expressions is a highly demanding task, especially in everyday life scenarios. Different sources of artifacts have to be considered in order to successfully extract the intended emotional nuances of the face. The exact and robust detection and orientation of faces impeded by occlusions, inhomogeneous lighting and fast movements is only one difficulty. Another one is the question of selecting suitable features for the application at hand. In the literature, a vast body of different visual features grouped into dynamic, spatial and textural families, has been proposed. These features exhibit different advantages/disadvantages over each other due to their inherent structure, and thus capture complementary information, which is a promising vantage point for fusion architectures. To combine different feature sets and exploit their respective advantages, an adaptive multilevel fusion architecture is proposed. The cascaded approach integrates information on different levels and time scales using artificial neural networks for adaptive weighting of propagated intermediate results. The performance of the proposed architecture is analysed on the GEMEP-FERA corpus as well as on a novel dataset obtained from an unconstrained, spontaneuous human-computer interaction scenario. The obtained performance is superior to single channels and basic fusion techniques.

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Section: • Compensation Of Missing Data Points Should Be Includedmentioning

confidence: 99%

Cascaded Fusion of Dynamic, Spatial, and Textural Feature Sets for Person-Independent Facial Emotion Recognition

Kächele¹,

Schwenker

2014

2014 22nd International Conference on Pattern Recognition

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“…Authors in [8] proposed a SVM-based approach to distinguish microcalcifications from other ROIs. A study on the performance of several classifiers is presented in [6], including SVM, ANN, Bayesian and kNN techniques.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

Alanís-Reyes¹,

Hernández-Cruz²,

Cepeda³

et al. 2012

JCT

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Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured

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“…Many computer aided diagnosis (CAD) methods have been developed for analyzing and interpreting the mammogram. In the mammograms [6][7][8], masses are the most important symptoms of abnormality. Accurate diagnosis of masses is very difficult because of their appearances.…”

Section: Introductionmentioning

confidence: 99%

ass Detection and Classification using Machine Learning Techniques in Digital Mammograms

Narasimhamurthy¹,

N²,

Sheshadri³

2013

IJCA

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Breast cancer is one of the most dangerous carcinomas for middle-aged and older women in the world. Mammography is a detection tool that assists the radiologists in reading the mammograms. In this paper, new techniques are proposed to detect and classify the masses automatically. These techniques improve the detection and classification process. Classification of masses into benign or malignant is an issue as the number of instances belongs to benign class is significantly greater than the malignant classes. This imbalanced problem is well addressed in proposed method using different approaches. This classification method outperforms many other classification approaches.

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On the Classification of Imbalanced Datasets

Cited by 23 publications

References 42 publications

Cascaded Fusion of Dynamic, Spatial, and Textural Feature Sets for Person-Independent Facial Emotion Recognition

Cascaded Fusion of Dynamic, Spatial, and Textural Feature Sets for Person-Independent Facial Emotion Recognition

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

ass Detection and Classification using Machine Learning Techniques in Digital Mammograms

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