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Analysis of RNNs and Different ML and DL Classifiers on Speech-Based Emotion Recognition System Using Linear and Nonlinear Features
Published in Amit Kumar Tyagi, Ajith Abraham, Recurrent Neural Networks, 2023
Shivesh Jha, Sanay Shah, Raj Ghamsani, Preet Sanghavi, Narendra M. Shekokar
In gradient boosting, decision trees are the weak learners. Decision trees can be imagined forming by iteratively asking questions to the data. To reduce the problem of overfitting, many decision trees are combined using the boosting methods. Boosting basically means getting a strong learner after combining sequentially many weak learners. Each tree focuses on improving the errors of the previous tree. Though it is a weak learner, combining all the weak learners together sequentially and focusing on the errors of previous trees results in a more accurate model. In a gradient-boosting algorithm, each new learner fits on the residuals from the previous weak learner. The aggregation of the results from each step by the final model results in a more accurate and efficient strong learner (Figure 7.5).
Implementation
Published in Seyedeh Leili Mirtaheri, Reza Shahbazian, Machine Learning Theory to Applications, 2022
Seyedeh Leili Mirtaheri, Reza Shahbazian
XGboost is an optimized distributed gradient boosting designed for high efficiency, flexibility, and portability [116]. It is an open-source library implementing the gradient boosting decision tree algorithm. It has become more popular recently since it was chosen by many winning Machine Learning teams in a lot of Machine Learning competitions. XGBoost uses the Gradient Boosting framework for implementing Machine Learning algorithms. It offers a parallel tree boosting (also known as GBDT or GBM), which solves several data science problems expeditiously. The same code runs on main distributed environments (MPI, SGE, Hadoop) and can solve a huge number of problems. The term gradient boosting comes from the idea of improving or boosting a weak model by mixing it with many other weak models hoping the result of generating an overall strong model. XGBoost boosts weak learning models using iterative learning. It offers interfaces for Python, Java, C++, Julia, and R and is compatible with Linux, Windows, and MacOS while supporting GPUs. XGBoost also supports distributed processing frameworks such as Apache Hadoop/Spark/Flink and Dataflow.
An Advanced Ensemble Approach to Household Poverty Level Prediction
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
AdaBoost also known as Adaptive Boosting is a process which combines several weak learners to give one strong learner. The model assigns weights to the outputs where more weight is given to outputs classified wrongly and less weight to correctly classified outputs and all these outputs from all the trees are ensembled for a final prediction. This is a weighted ensemble where weights to every tree are assigned according to the error rate. In Gradient Boosted trees, at each step the residuals or error is calculated and more weak learners are used to learn and concentrate on the residuals. Thus, GBTs are treated as an optimization problem where the main aim of the algorithm is to reduce or optimize the loss function. XgBoost and LightGBM are types of extreme gradient boosting techniques. The trees can have a varying number of terminal nodes and left weights of the trees that are calculated with less evidence is shrunk more heavily There is an extra randomisation parameter which helps to reduce the correlation between independent trees.
Streamflow prediction in ungauged basins located within data-scarce areas using XGBoost: role of feature engineering and explainability
Published in International Journal of River Basin Management, 2023
XGBoost is a machine learning technique based on gradient boosting (Chen & Guestrin, 2016). Weak learners often in the form of decision trees are used to continuously improve the previous learners. To avoid overfitting, regularization term is introduced in the objective function of XGBoost (Szczepanek, 2022): where is the objective function, is the regularization term at iteration k, is a constant, denotes complexity of leaves, is the number of leaves, is the penalty parameter and is the output of each leaf node.
Reinforced active learning for CVD-grown two-dimensional materials characterization
Published in IISE Transactions, 2023
Zebin Li, Fei Yao, Hongyue Sun
We use XGBoost as the binary classifier in our RAL framework. XGBoost is a powerful ML method proposed in 2016 (Chen and Guestrin, 2016). As an ensemble method originated from Gradient Boosting, it integrates several weak models into a strong model in an iterative additive way (Friedman, 2001). The weak model, i.e., the decision tree, uses a tree structure to organize a series of rules and to make predictions. Specifically, the nodes of the tree are the rules, and the leaves of the tree are the outputs (e.g., labels or probabilities) of the model. Then, the outputs of different decision trees are combined by voting or averaging to get the final result. Gradient Boosting uses the prediction residual to improve the current model and a new decision tree is built at each iteration. XGBoost further enhances the model performance by introducing an additional penalty term to the loss function which penalizes the model complexity.
Comparative Assessment of Regression Techniques for Wind Power Forecasting
Published in IETE Journal of Research, 2023
Rachna Pathak, Arnav Wadhwa, Poras Khetarpal, Neeraj Kumar
Boosting is an ensemble-based technique that iteratively trains a series of weak learners. It aims at reducing the errors from weakly learned models (classifiers and regressors). Weak models are those that have a high biasing towards the training data coupled with low variance and regularization, and whose results are considered only slightly better than random guesses. Boosting machines usually involve three components: a loss function, weak learners and an additive model. Gradient boosting produces a strong model based on a multitude of weak models (base learners) – typically decision trees. Gradient Boosting Machines work by identifying the limitations of weak models with the help of gradients. This is achieved via an iterative approach where the task is to ultimately combine base learners to reduce prediction errors, where trees are combined via the additive model while minimizing loss function through gradient descent.