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Adaptive Boosting Method for Mitigating Ethnicity and Age Group Unfairness
Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be...
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An adaptive CU size decision algorithm based on gradient boosting machines for 3D-HEVC inter-coding
3D high-efficiency video coding (3D-HEVC) is an extension of the HEVC standard for coding of texture videos and depth maps. 3D-HEVC inherits the same...
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Boosting
This chapter first introduces the idea of the boosting method and the representative boosting algorithm AdaBoost, then discusses why AdaBoost can...
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An optimized AdaBoost algorithm with atherosclerosis diagnostic applications: adaptive weight-adjustable boosting
In this study, a new boosting algorithm was proposed based on the traditional AdaBoost algorithm to better address classification problems. While...
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Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting
Infrared imaging technology has attracted numerous interests in military, security, transportation, etc. However, infrared images suffer from low...
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Adaptive Sampling for Weighted Log-Rank Survival Trees Boosting
The field of survival analysis is devoted to predicting the probability and time of the occurrence of an event. The global problem is to predict the...
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Efficient Colon Cancer Identification Based on Genetics Sequence Linear Support Vector Feature Selection Using Adaptive Ensemble Boosting Fuzzified Deep Neural Network
The human behaviors are varying depends on the DNA module presents in generation of human life styles. Increasing disease factors in due to DNA from...
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Boosting Adaptive Graph Augmented MLPs via Customized Knowledge Distillation
While Graph Neural Networks (GNNs) have shown convinced performance on handling non-Euclidean network data, the high inference latency caused by...
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Boosting for regression transfer via importance sampling
Current instance transfer learning (ITL) methodologies use domain adaptation and sub-space transformation to achieve successful transfer learning....
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TRBoost: a generic gradient boosting machine based on trust-region method
Gradient Boosting Machines (GBMs) have achieved remarkable success in effectively solving a wide range of problems by leveraging Taylor expansions in...
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Brain stroke prediction model based on boosting and stacking ensemble approach
The concern of brain stroke increases rapidly in young age groups daily. The leading causes of death from stroke globally will rise to 6.7 million...
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Advanced Persistent Threat Identification with Boosting and Explainable AI
Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The...
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Unveiling hidden factors: explainable AI for feature boosting in speech emotion recognition
Speech emotion recognition (SER) has gained significant attention due to its several application fields, such as mental health, education, and...
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Boosting the visibility of services in microservice architecture
Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on....
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An adaptive multi-class imbalanced classification framework based on ensemble methods and deep network
Data imbalance is one of the most difficult problems in machine learning. The improved ensemble learning model is a promising solution to mitigate...
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De-noising boosting methods for variable selection and estimation subject to error-prone variables
Boosting is one of the most powerful statistical learning methods that combines multiple weak learners into a strong learner. The main idea of...
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Ensemble: Bagging and Boosting
In the previous chapter, you learned the decision tree algorithm. Building efficient trees is usually a complex, time-consuming process, especially...
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An IoT-Based Heart Disease Diagnosis System Using Gradient Boosting and Deep Convolution Neural Network
The tremendous strides that have been made in biotechnology and the establishment of public healthcare infrastructures have resulted in a monumental...
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Boosting Multi-modal Ocular Recognition via Spatial Feature Reconstruction and Unsupervised Image Quality Estimation
In the daily application of an iris-recognition-at-a-distance (IAAD) system, many ocular images of low quality are acquired. As the iris part of...
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Boosting for Multivariate Longitudinal Responses
Boosting, a machine learning approach, has gained popularity over the years in its application to various types of data, including longitudinal data....
