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iCost: A Novel Instance Complexity-Based Cost-Sensitive Learning Framework
Background: Class imbalance in data presents significant challenges for classification tasks. It is fairly common and requires careful handling to obtain desirable performance. Traditional classification algorithms become biased toward the majority class. One way to alleviate the scenario is to make the classifiers cost-sensitive. This is achieved by assigning a higher misclassification cost to minority-class instances.
Research Gap: One issue with this implementation is that all the minority-class instances are treated equally, and assigned with the same penalty value. However, the learning difficulties of all the instances are not the same. Instances that are located in the overlapping region or near the decision boundary are harder to classify, whereas those further away are easier. Without taking into consideration the instance complexity and naively weighting all the minority-class samples uniformly, results in an unwarranted bias and consequently, a higher number of misclassifications of the majority-class instances.
Proposal: To alleviate the situation, we propose a novel instance complexity-based cost-sensitive approach (termed ’iCost’) in this study. We first categorize all the minority-class instances based on their difficulty level and then the instances are penalized accordingly. This ensures a more equitable instance weighting and prevents excessive penalization.
Results: The performance of the proposed approach is tested on 65 binary and 10 multiclass imbalanced datasets against the traditional cost-sensitive learning frameworks. A significant improvement in performance has been observed (displayed in the figure below), demonstrating the effectiveness of the proposed strategy.
Machine Learning Enabled Multimode Fiber Specklegram Sensors: A Review
Multimode fiber (MMF) speckle-gram sensors have recently drawn significant attention due to the incorporation of ML algorithms in detecting different sensing parameters. Deep learning techniques provide an efficient way of extracting information from fiber specklegrams, which can be utilized in different sensing applications.
CNNs have proven to be extremely successful in imaging technologies over the last decade. The breakthrough from CNN has instigated new frontiers for applications in different domains. CNNs can automatically learn the variations in MMF specklegrams under different conditions. Besides detecting slight variations in the fiber, CNNs are also insusceptible to environmental noise and fluctuations, thus, making them superior in terms of performance accuracy. They provide a low-cost and simple alternative to extracting information from fiber specklegrams which has piqued the interest of many researchers.
In this project, the articles that explore the use of ML in different MMF sensing applications, such as bending sensors, endoscopes, tactile or position sensors, and others, have been reviewed. The principle of specklegram in MMF, the data generation process, an overview of related DL approaches as well as open challenges and future research directions in this field have been discussed.
The project is supported by the Deanship of Research Oversight and Coordination (DROC) at King Fahd University of Petroleum and Minerals (KFUPM) through the Center for Communication Systems and Sensing grant no. SB201008.
The paper has been published in the IEEE Sensors Journal.
An ML-based decision support system for reliable diagnosis of ovarian cancer by leveraging explainable AI
This work reflects the application of AI in medical diagnosis and the transparency that is required for a reliable decision-support tool.
Novel Biomarkers: Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC.
Transparency: The stochastic nature of the ML algorithms as well as feature selection techniques raises concerns about the reliability of the system. To increase the trustworthiness and accountability of the diagnostic system as well as to provide transparency and explanations behind the predictions, explainable AI has been incorporated into the ML framework.
Results: The diagnostic accuracy obtained from the proposed system outperforms the existing methods as well as the state-of-the-art ROMA algorithm by a substantial margin.
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