An effective hybridized classifier for breast cancer diagnosis

Mittal, Dishant; Gaurav, Dev; Roy, Sanjiban Sekhar

doi:10.1109/aim.2015.7222674

Cited by 23 publications

(8 citation statements)

References 9 publications

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“…This approach is used to fit linear classifiers such as SVM and LR under convex loss functions. Mathematical representation is as follows [51]: Let (a i , b i ) be a set of training instances, a i belongs to Z n , b i belongs to -1, 1. The output of the classification is got by:…”

Section: Stochastic Gradient Descent Classifiermentioning

confidence: 99%

A New Ensemble-Based Intrusion Detection System for Internet of Things

et al. 2021

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The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

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Section: Stochastic Gradient Descent Classifiermentioning

confidence: 99%

A New Ensemble-Based Intrusion Detection System for Internet of Things

et al. 2021

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“…The combined model gave good accuracy. Mittal et al [10] presented a hybrid classifier for BC diagnosis. The classifier is a combination of self-organizing maps (SOM) and stochastic gradient descent (SGD) on WBCD.…”

Section: A Related Workmentioning

confidence: 99%

Breast cancer classification using machine learning techniques: a comparative study

Houfani

Slatnia

Kazar

et al. 2020

Mèd Technologies J.

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Background: The second leading deadliest disease affecting women worldwide, after lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed. These approaches show their effectiveness in data classification in many fields, especially in healthcare. Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification. The used dataset is Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification. Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are performant ( good accuracy specificity and sensitivity) compared to the other evaluated algorithms.

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“…This paper Also introduce the another three methods for diagnosis these are 1]decision tree, 2] random forests and 3] support vector machine. [8]. This paper proposed the method for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review

Nagtode¹,

Potdukhe²

2015

IJIPHM

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Human system is made up of many organs, brain is the first and the leading controller of the human system. Abnormal growth of the Overload cells in brain is called as brain tumor. In medical field brain tumor detection and classification is one of the difficult tasks. Early detection and classification of brain tumor obviously increases the chances of proper treatment. In this review paper specially focus on detection and classification of the brain tumor from MRI images of brain. Nowadays Medical Image Processing is a complex and challenging field. This review paper proposed the techniques this techniques is very useful to give the information about brain tumor which is very helpful to the doctor for giving the proper treatment.. This review paper proposed the 2D Gabor wavelet transform technique for detection of brain tumor. Proposed technique perform the systematic evaluation of the database of MRI brain images. Artificial neural network use for the Automated and accurate classification of MRI images of brain tumor. Artificial neural network Classify the Brain Tumor into three types that is benign, malignant or normal. Probabilistic Neural Network is the part of the artificial neural network. The performance of the Probabilistic Neural Network classifier evaluation is based on training set and accuracy of classification. Probabilistic Neural Network gives proper result of classification than other artificial neural networks and it is a optimistic tool for classification of the brain Tumors .

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An effective hybridized classifier for breast cancer diagnosis

Cited by 23 publications

References 9 publications

A New Ensemble-Based Intrusion Detection System for Internet of Things

A New Ensemble-Based Intrusion Detection System for Internet of Things

Breast cancer classification using machine learning techniques: a comparative study

Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review

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