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A new adaptive weighted deep forest algorithm which can be viewed as a modification of the confidence screening mechanism is proposed. The main idea underlying the algorithm is based on adaptive weigting of every training instance at each cascade level of the deep forest. The confidence screening mechanism for the deep forest proposed by Pang et al., strictly removes instances from training and testing processes to simplify the whole algorithm in accordance with the obtained random forest class probability distributions. This strict removal may lead to a very small number of training instances at the next levels of the deep forest cascade. The presented modification is more flexible and assigns weights to instances in order to differentiate their use in building decision trees at every level of the deep forest cascade. It overcomes the main disadvantage of the confidence screening mechanism. The proposed modification is similar to the AdaBoost algorithm to some extent. Numerical experiments illustrate the outperformance of the proposed modification in comparison with the original deep forest. It is also illustrated how the proposed algorithm can be extended for solving the transfer learning and distance metric learning problems.

A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted averaging where the weights are assigned to every tree and can be viewed as training parameters which are computed in an optimal way by solving a standard quadratic optimization problem maximizing Harrell’s C-index. Numerical examples with real data illustrate the outperformance of the proposed model in comparison with the original random survival forest.

Semi-supervised learning is a combination of conventional supervised methods with weakly supervised learning. A recent development in neural networks allows to achieve high-quality results but the training requires a large amount of annotated examples. This hinders the applicability of deep learning to some problems, especially medical imaging. In this paper, we present a semi-supervised learning approach based on convolutional neural networks (CNN) for medical image segmentation. A network is trained on a combination of fully labeled samples that have segmentation masks available and weakly labeled samples that only have class labels. We performed experiments that compare the results of the semi-supervised model with the baseline supervised method. Experiment results show the superiority of suggested methods on a low amount of fully annotated samples for lung nodules CT images.

A modification of the confidence screening mechanism based on adaptive weighing of every training instance at each cascade level of the Deep Forest is proposed. The modification aims to increase the classification accuracy. It is carried out by assigning weights to training instances at each forest cascade level in accordance with their classification accuracy. Larger values of accuracy produce smaller weights. Two strategies for using the weights are considered. The first one when the weights are regarded as probabilities of choosing the corresponding instances in building decision trees. According to the second strategy, the weights are used in splitting rules. The modification increases the classification accuracy and may reduce the training time for many real datasets. Numerical experiments illustrate good performance of the proposed modification in comparison with the original Deep Forest proposed by Zhou and Feng.

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions.