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Most of existing research works in the field of feature selection via matrix factorization techniques have been employed for unsupervised learning problems. This paper introduces a new framework for the supervised feature selection, called supervised feature selection by constituting a basis for the original space of features and matrix factorization (SFS-BMF). To this end, SFS-BMF is a guided search to find a basis for the original space of features that inherently contains linearly independent features and can be replaced with the original space. For finding the best subset of features regarding the class attribute, information gain is utilized for the process of constructing a basis. In fact, a basis for the original features is constructed according to the most informative features in terms of the information gain. Then, this basis is decomposed through a matrix factorization form in order to select a subset of features. Our proposed method guarantees the maximum relevancy of selected features to the output by using the information gain while simultaneously secures the minimum redundancy among them based on the linear independence property. Several experiments on high-dimensional microarray datasets are conducted for illustrating the efficiency of SFS-BMF. The experimental results show that the proposed SFS-BMF method outperforms some state-of-the-art feature selection methods with respect to classification performance and also according to the computational complexity.

The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features inin vitro, in vivoand clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the Matrix Factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other matrix factorization methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a Tyrosine Kinase Inhibitor (TKI) treatment response in the Cancer Cell Line Encyclopedia (CCLE).Key PointsMatrix Factorization (MF) is a useful framework for high dimensionality reduction in systems pharmacology.Novel feature selection methods using the incorporation of the mathematical conception of a basis for features into MF increases the performance of feature selection process.Feature selection based on the basis-concept in MF can provide predictive gene signatures for therapeutic responses in systems pharmacology.

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

Recently many neural network methods have been proposed for multi-label classification in the literature. One of these recent researches is the multi-layer extreme learning machines (ML-ELM) in which stack auto encoders have been used for tuning the weights. However, ML-ELM suffers from three primary drawbacks: First, input weights and biases are chosen randomly. Second, the pseudo-inverse solution for calculating output weights will cause to increase the reconstruction error. Third, memory and execution time of transformation matrices are proportional to the number of hidden layers. In this paper multi-layer kernel extreme learning machine, that uses a linear combination of base kernels in each layer, is proposed for multi-label classification. The proposed approach effectively addresses the above-mentioned drawbacks. Furthermore, multi-label classification data inherently have multi-modal aspects due to the variety of labels assigned to each instance. Applying a combination of different kernels is the added advantage of ML-CK-ELM that leads to implicitly assess the inherent multi-modal aspects of multi-label data; each kernel can be effectively used to cover one of the modals better than the other kernels. The empirical study indicates that ML-CK-ELM represents competitive performance against other state-of-the-art methods, and experimental results over multi-label datasets verify the feasibility of ML-CK-ELM.

In this paper, three novel frameworks based on the widespread regression methods Ridge, LASSO and Elastic Net are established to perform the task of feature selection. The suggested frameworks, which benefit from the joint advantages of the dual-manifold learning and the hesitant fuzzy correlation (HFC), utilize the concept of the hesitant fuzzy correlation matrix (HFCM) of the features and samples. In order to compute the HFCM, a fuzzy clustering algorithm is applied to samples (or features), and a hesitant fuzzy set on each sample (or each feature) is obtained after projecting the cluster membership functions on different samples (or features). Then, two kinds of HFCMs are calculated for samples of each class and the whole of features, respectively. In specific, a feature manifold regularization term based on the HFCM among features is added to the objective function in order to preserve the similarity between the features and the feature weights. Furthermore, a sample manifold regularization term is also considered for the purpose of preserving the local correlation among the samples of each class. Eventually, a set of experiments are conducted on twenty-four datasets in order to validate the performance of each method. The results confirm that the proposed approaches are effective to select suitable features in terms of both the number of selected features and the classification accuracy.

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Multi-label learning has been received much attention due to its applicability in machine learning problems. In current years, quite few approaches based on either extreme learning machine (ELM) or radial basis function (RBF) neural network have been proposed with the aim of increasing the efficiency of the multi-label classification. Most existing multi-label learning algorithms focus on information about the feature space. In this paper, our major intention is to regularize the objective function of multi-label learning methods via Locally Linear Embedding (LLE). To achieve this goal, two neural network architectures namely Multi-Label RBF (ML-RBF) and Multi-Label Multi Layer ELM (ML-ELM) are utilized. Then, a regularized multi-label learning method via feature manifold learning (RMLFM) and a regularized multi-label learning method via dual-manifold learning (RMLDM) are established for training two network structures. RMLDM simultaneously exploits the geometry structure of both feature and data space. Furthermore, eight different configurations of applying training algorithms (i.e., RMLFM and RMLDM) to model architectures (i.e., ML-RBF and ML-ELM) are considered for conducting comparisons. The validity and effectiveness of these eight classifiers are indicated by a number of experimental studies on several multi-label datasets. Furthermore, the experiments indicate that the efficiency of the classification can be improved considerably against some cutting-the-edge multi-label techniques for the neural classifiers in which the dual-manifold learning is used as the training method.

In this paper, the Ridge, LASSO and Elastic Net regression methods are adapted for the task of selecting feature. In order to enhance the feature selection performance via these methods, a Hesitant Fuzzy Correlation Matrix (HFCM) is added to the objective functions of these models for addressing the minimum redundancy of features. To this end, the fuzzy C-means clustering is utilized, and the obtained fuzzy clusters are projected on the features in a way that the number of fuzzy Membership Functions (MF) for each feature is equal to the number of clusters. Then, the projected MFs on each feature are considered as a Hesitant Fuzzy Set (HFS), and thereby the hesitant fuzzy correlation between features is calculated. Afterward, the obtained HFCM is employed in the regression methods for securing the minimum redundancy of features. Eventually, the accuracies of the selected features, achieved by these methods, are determined by three different classification models such as Naive Bayes, SVM and Decision Tree. A large number of experiments are conducted over twenty-four classification datasets to demonstrate the efficiency and applicability of using HFCM in some classical regression methods.