Transfer Learning for Cross-Dataset Recognition: A Survey <s> Imbalanced Unlabelled Target Dataset <s> In machine learning problems, differences in prior class probabilities -- or class imbalances -- have been reported to hinder the performance of some standard classifiers, such as decision trees. This paper presents a systematic study aimed at answering three different questions. First, we attempt to understand the nature of the class imbalance problem by establishing a relationship between concept complexity, size of the training set and class imbalance level. Second, we discuss several basic re-sampling or cost-modifying methods previously proposed to deal with the class imbalance problem and compare their effectiveness. The results obtained by such methods on artificial domains are linked to results in real-world domains. Finally, we investigate the assumption that the class imbalance problem does not only affect decision tree systems but also affects other classification systems such as Neural Networks and Support Vector Machines. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Imbalanced Unlabelled Target Dataset <s> We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Imbalanced Unlabelled Target Dataset <s> Let X denote the feature and Y the target. We consider domain adaptation under three possible scenarios: (1) the marginal PY changes, while the conditional PX/Y stays the same (target shift), (2) the marginal PY is fixed, while the conditional PX/Y changes with certain constraints (conditional shift), and (3) the marginal PY changes, and the conditional PX/Y changes with constraints (generalized target shift). Using background knowledge, causal interpretations allow us to determine the correct situation for a problem at hand. We exploit importance reweighting or sample transformation to find the learning machine that works well on test data, and propose to estimate the weights or transformations by reweighting or transforming training data to reproduce the covariate distribution on the test domain. Thanks to kernel embedding of conditional as well as marginal distributions, the proposed approaches avoid distribution estimation, and are applicable for high-dimensional problems. Numerical evaluations on synthetic and real-world data sets demonstrate the effectiveness of the proposed framework. <s> BIB003
This problem assumes the target domain is class imbalanced and only with unlabelled data. Thus, the statistic criterion can be used. This problem is known as prior probability shift, or imbalanced data in the context of classification. The imbalanced data issue is quite common in practice. For example, the abnormal actions (kick, punch, or fall down) are generally much rarer than normal actions in video surveillance but generally require higher recognition rate. Feature Representation-based Transfer. In the classification scenario, the prior probability (P (Y )) shift was referred to the class imbalance problem BIB001 BIB003 . The statistic criterion can be used. BIB003 assume the P (Y ) between source and target datasets are different, but the source set is richer than the target set, such that the support of P (Y ) target is contained in the support of P (Y ) source . Then they tackle the prior probability shift by re-weighting the source samples using similar idea as Kernel Mean Matching method BIB002 . They also define the situation where both P (Y ) and P (X|Y ) change across datasets and propose a kernel approach to re-weight and transform the source data to reduce the distribution shift by assuming that the source domain can be transferred to the target domain by location-scale (LS) transformation (the P (X|Y ) source and P (X|Y ) source only differs in the location and scale). Rather than assuming that all the features can be transferred to the target domain by LS transformation, propose to learn the conditional invariant components by a linear transformation, and then re-weight the source domain data to reduce shift of P (Y ) and P (Y |X) between domains. Recently, introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains. The proposed weighted MMD model is defined by introducing an auxiliary weight for each class in the source domain, and a classification EM algorithm is suggested by alternating between assigning the pseudo-labels, estimating auxiliary weights and updating model parameters.