You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on Quantification Learning 
Chapter Title:  Ensembles for Quantification Learning 
1. Abstract of Cited Paper (BIB001):  Our hypothesis: ensembles are well suited for problems with distribution changes.If those changes are characterizable, ensembles can be designed to tackle them.Idea: to generate different samples based on the expected distribution changes.Case study: we present ensembles versions of two binary quantification algorithms.Ensembles outperform original counterpart algorithms using trivial aggregation rules. Display Omitted Ensemble methods are widely applied to supervised learning tasks. Based on a simple strategy they often achieve good performance, especially when the single models comprising the ensemble are diverse. Diversity can be introduced into the ensemble by creating different training samples for each model. In that case, each model is trained with a data distribution that may be different from the original training set distribution. Following that idea, this paper analyzes the hypothesis that ensembles can be especially appropriate in problems that: (i) suffer from distribution changes, (ii) it is possible to characterize those changes beforehand. The idea consists in generating different training samples based on the expected distribution changes, and to train one model with each of them. As a case study, we shall focus on binary quantification problems, introducing ensembles versions for two well-known quantification algorithms. Experimental results show that these ensemble adaptations outperform the original counterpart algorithms, even when trivial aggregation rules are used.

Literature Review:
(1) Sample generation. This step generates the samples for training the models of the ensemble. Each training sample has the same size that the training dataset, but its prevalence is different. The process to generate each sample works as follows. First, p j , the sample prevalence, is fixed randomly in a predefined range [p min , p max ] that depends on the expected drift in the distribution. Then, the examples for the sample are selected using random sampling with replacement (to guarantee that P (x |y) is constant) until the distribution [p j , 1 − p j ] is obtained. (2) Training phase. The quantifiers of the ensemble, {h 1 , . . . ,h k }, are learned by training the selected quantification algorithm over each generated training sample. Two different quantification methods are considered in the article: AC (Equation (32)) (see Section 6.2) and HDy (see Section 8.4). (3) Prediction. Each modelh j of the ensemble is applied to obtain the prevalence estimate, p j , of the unlabeled test set T . The proposed aggregation function is the arithmetic mean, thus the final prediction is just mean(p 1 , . . . ,p k ). The authors argue that the application of an ensemble approach to quantification tasks should produce more advantages than its use in other learning problems, for example, classification. The advantage of using a collection of models instead of a single one is common to all learning problems where ensemble algorithms are applied: an individual model presents the risk of performing poorly in some situations; theoretically, an ensemble reduces such risk. However, there are additional benefits in the quantification case: (i) The knowledge about the learning problem helps to introduce an adequate diversity into the ensemble and (ii) using multiple models reduces the issues of the base quantification algorithm, especially for those methods that correct their estimates. The AC correction (Equation (32)) is sometimes unstable, despite it theoretically returns perfect estimations. When the estimates of tpr and f pr are inaccurate, the correction will cause inappropriate changes BIB001 BIB002 ) (this is in part the motivation of the methods in Section 6.5). Using ensembles reduces both risks: the risk of having a bad single classifier and the risk of a poor posterior correction.