Problem Of Missing Features Research Articles

Research on prediction of human oral bioavailability of drugs based on improved deep forest

Human oral bioavailability is a crucial factor in drug discovery. In recent years, researchers have constructed a variety of different prediction models. However, given the limited size of human oral bioavailability data sets, the challenge of making accurate predictions with small sample sizes has become a critical issue in the field. The deep forest model, with its adaptively determinable number of cascade levels, can perform exceptionally well even on small-scale data. However, the original deep forest suffers unbalanced multi-grained scanning process and premature stopping of cascade forest training. In this paper, we propose a human oral bioavailability predict method based on an improved deep forest, called balanced multi-grained scanning mapping cascade forest (bgmc-forest). Firstly, the mordred descriptor method is selected to feature extraction, then enhanced features are obtained by the improved balanced multi-grained scanning, which solves the problem of missing features at both ends. And finally, the prediction results are obtained by feature mapping cascaded forests, which is based on principal component analysis and cascade forests, ensures the effectiveness of the cascade forest. The superiority of the model constructed in this paper is demonstrated through comparative experiments, while the effectiveness of the improved module is verified through ablation experiments. Finally the decision-making process of the model is explained by the shapley additive explanations interpretation algorithm.

Small target detection algorithm based on multi-branch stacking and new sampling transition module.

Aiming at the problem that the SSD algorithm does not fully extract the feature information contained in each feature layer, as well as the feature information is easily lost during the sampling process, which makes the feature expression ineffective and leads to insufficient performance in small target detection. In this paper, AMT-SSD is proposed, a small target detection algorithm that incorporates the multi-branch stacking and new sampling transition module of the attention mechanism. In this algorithm, the composite attention mechanism is utilized to improve the correlation of features of the samples to be detected in terms of spatial and channels, and the efficiency of the algorithm; secondly, multi-branch stacking module is used to extract multi-size features for each feature layer, and different sizes of convolution kernels are utilized in parallel to fully extract their features and improve the expression of features; meanwhile, during the sampling process, the problem of missing features is solved by applying inverse subpixel convolution in the new sampling transition module. Experimentally, the AMT-SSD algorithm achieves 84.6% and 53.4% mAP metrics on the PASCAL VOC dataset and MS COCO dataset, respectively. This indicates that the AMT-SSD algorithm can effectively extract feature information that is beneficial to detection samples, and also performs well in reducing feature loss, which is effective for the algorithm to improve the algorithm on small targets.

HetReGAT-FC: Heterogeneous Residual Graph Attention Network via Feature Completion

Heterogeneous graph embedding is receiving increasing attention from researchers due to the ubiquity of heterogeneous graphs (HGs). How to effectively handle the problem of missing features in HGs has become a hot research topic in recent years. However, the existing heterogeneous graph neural networks (HGNNs) with feature completion are facing two challenges: (1) Pre-training is required; and (2) Heterogeneous information in the HG is not fully explored. To this end, we propose a Heterogeneous Residual Graph Attention Network via Feature Completion (HetReGAT-FC). Specifically, we first design a Heterogeneous Residual Graph Attention Network (HetReGAT) to learn topological information. Then, the attention mechanism is adopted to complete the missing features. Finally, HetReGAT is used to learn the final node embeddings on the feature completed heterogeneous graph. To prove the effectiveness of this work, we conduct extensive experiments on three real-world datasets and compare it with ten competitive baselines. The results demonstrate that the proposed HetReGAT-FC significantly outperforms state-of-the-art methods. The codes and data of this work are available at https://github.com/ZZY-GraphMiningLab/HetReGAT-FC.

Gesture recognition of continuous wavelet transform and deep convolution attention network.

To solve the problem of missing data features using a deep convolutional neural network (DCNN), this paper proposes an improved gesture recognition method. The method first extracts the time-frequency spectrogram of surface electromyography (sEMG) using the continuous wavelet transform. Then, the Spatial Attention Module (SAM) is introduced to construct the DCNN-SAM model. The residual module is embedded to improve the feature representation of relevant regions, and reduces the problem of missing features. Finally, experiments with 10 different gestures are done for verification. The results validate that the recognition accuracy of the improved method is 96.1%. Compared with the DCNN, the accuracy is improved by about 6 percentage points.

Multi-label learning with missing features and labels and its application to text categorization

• This paper proposes a new method MMFL to address the problem of missing features and missing labels simultaneously in multi-label learning. • Matrix decomposition technology is used to recover the missing values of features and labels. In addition, to overcome the problem of tail labels in matrix factorization, an extra classifier for the sparse tail labels is built. • The experiment on nine multi-label text categorization data sets show that our proposed method achieves a competitive performance in multi-label learning with missing features and labels. In multi-label learning, researchers usually assume that the training data set is complete. However, this assumption is not always hold in real applications, e.g., the features or labels are missing for some data examples. Existing algorithms mainly focus on the problem of missing labels, and ignore the problem of missing features. This paper proposes a novel multi-label learning algorithm named MMFL, i.e., Multi-label learning with Missing Features and Labels, which can deal with the problem of missing features and labels simultaneously. First, we try to recover the missing values of features and labels by matrix factorization, and then learn a classification model from the latent feature space to the latent label space. Second, to overcome the problem of tail labels in matrix factorization, we build an extra classifier for the sparse tail labels. Besides, the manifold regularization technology is used to keep the manifold structures of instance similarity and label correlation. The effectiveness of our proposed method is verified by comparing it with the state-of-the-art approaches over eight multi-label benchmark data sets.

Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling.

Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.

On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics.

In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution the audience not to take this assumption for granted, and present empirical evidence of its ineffectiveness from a machine learning perspective. Our examination is based on a setting where binding profiles are used as features to train predictive models; we show (1) prediction performance degrades when the assumption fails and (2) explicit recovery of unreported profiles improves prediction performance. In particular, we propose a framework that jointly recovers profiles and learns predictive model, and show it achieves further performance improvement. The presented study not only suggests applying matrix recovery methods to recover unreported profiles, but also initiates a new missing feature problem which we called Learning with Positive and Unknown Features.

A touchless interaction interface for observing medical imaging

Using volume rendering to generate 3D models is associated with the problem of missing features on areas of interest, which are possibly concealed by other information. This article presents a novel focus-and-context medical imaging observation system using gesture-based technique to build a touchless interactive environment. The system offers two types of medical imaging observation tool, namely, 3D section cutting tool and 3-axes cross-section synchronization tool, enabling users to quickly and easily observe tissue sections. Feature classification was achieved using region growing and size-based transfer approaches. Combined with view penetration function (cylinder and cone view penetration functions), the system allows for direct observation of hidden features. The analytical experimental results verified that the proposed system is easy to operate in a touchless environment and creates positive user experience regarding observation and interaction.

A feature weighted penalty based dissimilarity measure for k-nearest neighbor classification with missing features

The k-Nearest Neighbor (kNN) classifier is an elegant learning algorithm widely used because of its simple and non-parametric nature. However, like most learning algorithms, kNN cannot be directly applied to data plagued by missing features. We make use of the philosophy of a Penalized Dissimilarity Measure (PDM) and incorporate a PDM called the Feature Weighted Penalty based Dissimilarity (FWPD) into kNN, forming the kNN-FWPD classifier which can be directly applied to datasets with missing features, without any preprocessing (like marginalization or imputation). Extensive experimentation on simulations of four different missing feature mechanisms (using various datasets) suggests that the proposed method can handle the missing feature problem much more effectively compared to some of the popular imputation mechanisms (used in conjunction with kNN).

A classifier ensemble approach for the missing feature problem

Many classification problems must deal with data that contains missing values. In such cases data imputation is critical. This paper evaluates the performance of several statistical and machine learning imputation methods, including our novel multiple imputation ensemble approach, using different datasets. Several state-of-the-art approaches are compared using different datasets. Some state-of-the-art classifiers (including support vector machines and input decimated ensembles) are tested with several imputation methods. The novel approach proposed in this work is a multiple imputation method based on random subspace, where each missing value is calculated considering a different cluster of the data. We have used a fuzzy clustering approach for the clustering algorithm. Our experiments have shown that the proposed multiple imputation approach based on clustering and a random subspace classifier outperforms several other state-of-the-art approaches. Using the Wilcoxon signed-rank test (reject the null hypothesis, level of significance 0.05) we have shown that the proposed best approach is outperformed by the classifier trained using the original data (i.e., without missing values) only when >20% of the data are missed. Moreover, we have shown that coupling an imputation method with our cluster based imputation we outperform the base method (level of significance ∼0.05). Starting from the assumptions that the feature set must be partially redundant and that the redundancy is distributed randomly over the feature set, we have proposed a method that works quite well even when a large percentage of the features is missing (≥30%). Our best approach is available (MATLAB code) at bias.csr.unibo.it/nanni/MI.rar.

Learn ++.MF: A random subspace approach for the missing feature problem

We introduce Learn ++.MF, an ensemble-of-classifiers based algorithm that employs random subspace selection to address the missing feature problem in supervised classification. Unlike most established approaches, Learn ++.MF does not replace missing values with estimated ones, and hence does not need specific assumptions on the underlying data distribution. Instead, it trains an ensemble of classifiers, each on a random subset of the available features. Instances with missing values are classified by the majority voting of those classifiers whose training data did not include the missing features. We show that Learn ++.MF can accommodate substantial amount of missing data, and with only gradual decline in performance as the amount of missing data increases. We also analyze the effect of the cardinality of the random feature subsets, and the ensemble size on algorithm performance. Finally, we discuss the conditions under which the proposed approach is most effective.

Bayesian classification and feature reduction using uniform dirichlet priors

In this paper, a method of classification referred to as the Bayesian data reduction algorithm (BDRA) is developed. The algorithm is based on the assumption that the discrete symbol probabilities of each class are a priori uniformly Dirichlet distributed, and it employs a "greedy" approach (which is similar to a backward sequential feature search) for reducing irrelevant features from the training data of each class. Notice that reducing irrelevant features is synonymous here with selecting those features that provide best classification performance; the metric for making data-reducing decisions is an analytic for the probability of error conditioned on the training data. To illustrate its performance, the BDRA is applied both to simulated and to real data, and it is also compared to other classification methods. Further, the algorithm is extended to deal with the problem of missing features in the data. Results demonstrate that the BDRA performs well despite its relative simplicity. This is significant because the BDRA differs from many other classifiers; as opposed to adjusting the model to obtain a "best fit" for the data, the data, through its quantization, is itself adjusted.