On Expert-Defined Versus Learned Hierarchies for Image Classification

Panchenko, Ivan; Khan, Adil

doi:10.1007/978-3-030-37334-4_31

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…It is widely used in different domains to make decisions or predictions without giving much effort to programming. These AI algorithms are widely used in different applications such as image processing [1,2], anomaly detection [3] and so on. The deep learning [4] approaches are the recent evolution of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network-based Cross-Project Fault Prediction

Pal

2021

Preprint

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Background: The early stage of defect prediction in the software development life cycle can reduce testing effort and ensure the quality of software. Due to the lack of historical data within the same project, Cross-Project Defect Prediction (CPDP) has become a popular research topic among researchers. CPDP trained classifiers based on labeled data sets of one project to predict fault in another project. Goals: Software Defect Prediction (SDP) data sets consist of manually designed static features, which are software metrics. In CPDP, source and target project data divergence is the major challenge in achieving high performance. In this paper, we propose a Generative Adversarial Network (GAN)-based data transformation to reduce data divergence between source and target projects. Method: We apply the Generative Adversarial Method where label data sets are choosing as real data, while target data sets are choosing as fake data. The Discriminator tries to measure the perfection of domain adaptation through loss function. Through the generator, target data sets try to adapt the source project domain and, finally, apply machine learning classifier (i.e., Naive Bayes) to classify faulty modules. Results: Our result shows that it is possible to predict defects based on the Generative Adversarial Method. Our model performs quite well in a cross-project environment when we choose JDT as a target data sets. However, all chosen data sets are facing a large class imbalance problem which affects the performance of our model.

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Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network-based Cross-Project Fault Prediction

Pal

2021

Preprint

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“…Deep Learning(DL) [45] is a subcategory of ML, which consists of algorithms containing multiple processing layers that are self-capable of handling unstructured or unlabeled data [36], based on hierarchy of data [40]. DL models learn the features of the data at every layer, and those layers are trained iteratively.…”

Section: Introductionmentioning

confidence: 99%

What augmentations are sensitive to hyper-parameters and why?

Muhammad¹,

Bekkouch²

2021

Preprint

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We apply augmentations to our dataset to enhance the quality of our predictions and make our final models more resilient to noisy data and domain drifts. Yet the question remains, how are these augmentations going to perform with different hyper-parameters? In this study we evaluate the sensitivity of augmentations with regards to the model's hyper parameters along with their consistency and influence by performing a Local Surrogate (LIME) interpretation on the impact of hyperparameters when different augmentations are applied to a machine learning model. We have utilized Linear regression coefficients for weighing each augmentation. Our research has proved that there are some augmentations which are highly sensitive to hyper-parameters and others which are more resilient and reliable.

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What Augmentations are Sensitive to Hyper-Parameters and Why?

Muhammad¹,

Bekkouch²

2022

Lecture Notes in Networks and Systems

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On Expert-Defined Versus Learned Hierarchies for Image Classification

Cited by 5 publications

References 14 publications

Generative Adversarial Network-based Cross-Project Fault Prediction

Generative Adversarial Network-based Cross-Project Fault Prediction

What augmentations are sensitive to hyper-parameters and why?

What Augmentations are Sensitive to Hyper-Parameters and Why?

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