Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique

Devi, Debashree; Biswas, Saroj Kumar; Purkayastha, Biswajit

doi:10.1080/09540091.2018.1560394

Cited by 58 publications

(27 citation statements)

References 31 publications

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“…Furthermore, larger datasets should commonly be splitted using crossvalidation to approximate external validity thus generate better outcomes [68]. Simultaneously, findings need to be transferred to other types of diseases, for example, different types of cancer [67,87,[111][112][113]118], but also to other clinical application [100,114,115,117]. In addition, one must ask why scientific evidence is not yet widely integrated into disease diagnostics, for instance, in hospitals or other clinical environments.…”

Section: Corroboration and Portabilitymentioning

confidence: 99%

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

2021

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The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

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Section: Corroboration and Portabilitymentioning

confidence: 99%

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

2021

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“…This technique is able to detect new intrusions since we do not need to train this algorithm with attacks samples. One class SVM is effective on imbalanced data sets, as explored by Devi et al [47] • Long Short-Term Memory (LSTM) is a recurrent neural network, meaning that there are some recurrent connections in the network. Thus, recurrent networks have two sources of input: present data and data that has already been through the network.…”

Section: Classification Algorithmmentioning

confidence: 99%

Multi-level host-based intrusion detection system for Internet of things

et al. 2020

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The growth of the Internet of things (IoT) has ushered in a new area of inter-connectivity and innovation in the home. Many devices, once separate, can now be interacted with remotely, improving efficiency and organization. This, however, comes at the cost of rising security vulnerabilities. Vendors are competing to create and release quickly innovative connected objects, without focusing on the security issues. As a consequence, attacks involving smart devices, or targeting them, are proliferating, creating threats to user’s privacy and even their physical security. Additionally, the heterogeneous technologies involved in IoT make attempts to develop protection on smart devices much harder. Most of the intrusion detection systems developed for those platforms are based on network activity. However, on many systems, intrusions cannot easily or reliably be detected from network traces. We propose a novel host-based automated framework for intrusion detection. Our work combines user space and kernel space information and machine learning techniques to detect various kinds of intrusions in smart devices. Our solution use tracing techniques to automatically get devices behavior, process this data into numeric arrays to train several machine learning algorithms, and raise alerts whenever an intrusion is found. We implemented several machine learning algorithms, including deep learning ones, to achieve high detection capabilities, while adding little overhead on the monitored devices. We tested our solution within a realistic home automation system with actual threats.

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“…Debashree and coworkers [26] proposed a modification of the Tomek-Link undersampling method. They present a solution to class imbalance and classes overlapping, as these two problems affect the performance of standard classifiers.…”

Section: Related Workmentioning

confidence: 99%

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

et al. 2020

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Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data.

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Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique

Cited by 58 publications

References 31 publications

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Multi-level host-based intrusion detection system for Internet of things

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

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