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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.

A modification of the Deep Forest or gcForest proposed by Zhou and Feng for solving classification problems is proposed in the paper and called as PM-DF. The main idea for improving classification performance of the Deep Forest is to assign weights to subsets of the class probability distributions at the leaf nodes computed for every training example. The subsets of probability distributions are defined by using Walley’s imprecise pari-mutuel model which compactly divides the unit simplex of probabilities into subsets and allows us to simplify the algorithm of the weight calculation. The weights of the distribution subsets can be viewed in this case as second-order probabilities over subsets of the probability simplex. The optimal weights are computed by solving the standard quadratic optimization problem. The numerical experiments illustrate PM-DF.

Two new survival models, the deep survival forest and the Elastic-Net-Cox Cascade, are presented in the paper. They can be regarded as a combination of random survival forests and the Elastic-Net-Cox models with the deep forest (DF) proposed by Zhou and Feng. The main ideas to construct the models are to replace the original random forests incorporated into the DF with the corresponding survival analysis models. A stacking algorithm implemented in the deep survival forest and the Elastic-Net-Cox Cascade, which can be regarded as a link between the DF levels, uses quantiles of the random time-to-event and the mean time-to-event computed from the estimated survival functions at every level of the DF. Numerical examples with real data illustrate the proposed models.

This chapter reviews the basics and recent researches of computer-aided diagnosis (CAD) systems for assisting neuroradiologists in the detection, monitoring and prediction of multiple sclerosis (MS) in magnetic resonance (MR) images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology and conclude with possibilities in the future of the CAD systems for MS in MR images.

A very simple algorithm for explaining decisions of cancer computer-aided diagnosis systems is proposed. The algorithm produces explanations of diseases in the form of special sentences via natural language. It consists of two parts. The first part is implemented by using a standard local post-hoc explanation model, for example, the well-known method LIME. This part aims to select important features from a segmented and detected suspicious object or its low-dimensional feature representation. The second part is a set of simple classifiers which transform the selected important features into one of the classes corresponding to simple phrases. The explanation sentences in natural language are composed of the simple phrases (primitives) such that every subset of the phrases describes a single peculiarity of a suspicious object, for example, a shape, a structure, inclusions, contours. The primitives are regarded as classes for the set of classifiers. The proposed algorithm is general and can be applied to implementing the explanation subsystem of various diseases.

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.

Two algorithms for explaining decisions of a lung cancer computer-aided diagnosis system are proposed. Their main peculiarity is that they produce explanations of diseases in the form of special sentences via natural language. The algorithms consist of two parts. The first part is a standard local post-hoc explanation model, for example, the well-known LIME, which is used for selecting important features from a special feature representation of the segmented lung suspicious objects. This part is identical for both algorithms. The second part is a model which aims to connect selected important features and to transform them to explanation sentences in natural language. This part is implemented differently for both algorithms. The training phase of the first algorithm uses a special vocabulary of simple phrases which produce sentences and their embeddings. The second algorithm significantly simplifies some parts of the first algorithm and reduces the explanation problem to a set of simple classifiers. The basic idea behind the improvement is to represent every simple phrase from vocabulary as a class of the "sparse" histograms. An implementation of the second algorithm is shown in detail.

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.