Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning.

COVID-19 has become the most devastating disease of the current century and is pandemic. As per WHO report, there are globally 31,174,627 confirmed cases including 962,613 deaths as of 22 nd September,2020. The disease is spreading through outbreaks despite the availability of latest technologies for treatment of patients. In this paper, we proposed a neural network-based prediction of number of cases in India due to COVID-19. Recurrent neural network (RNN) based LSTM is applied on India dataset for prediction. LSTM networks are a type of RNN capable of learning order dependence in sequence forecasting problems. We analyze the performance of the network and then compare it with two parameter reduced variants of LSTM, obtained by elimination of hidden unit signals, bias and input signal. For performance evaluation, we used the MSE measure.

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In this paper, we show that the power function of sub E-function f n (x) is sub E-function. Furthermore, we establish some new integral inequalities of Hadamard type involving sub E-functions and concave E-functions.

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Transductive learning is a special case of semi-supervised learning, where class labels to the test patterns alone are found. That is, the domain of the learner is the test set alone. Often, transductive learners achieve a better classification accuracy, since additional information in the form of test patterns location in the feature-space is used. For several inductive learners, there exists corresponding transductive learners; like for SVMs there exists transductive SVMs (TSVMs). For nearest neighbor based classifiers, their corresponding transductive methods are achieved through graph mincuts or spectral graph mincuts. It is shown that these solutions achieve low leave-one-out cross-validation (LOOCV) error with respect to nearest neighbor based classifiers. It is formally shown in the paper that, through a clustering method, it is possible to get various solutions having zero LOOCV error with respect to nearest neighbor based classifiers. Some solutions can have low classification accuracy. The paper proposes, instead of optimizing LOOCV error, to optimize a margin like criterion. This criterion is based on the observation that similar labeled patterns should be nearer to each other, while dissimilar labeled patterns should be far away. An approximate method to solve the proposed optimization problem is given in the paper which is called selective incremental transductive nearest neighbor classifier (SI-TNNC). SI-TNNC finds the test pattern from the test set which is very close to one class of training patterns and at the same time very much away from the other class of training examples. The selected test pattern is given its nearest neighbor's label and is added to the training set. This pattern is removed from the test set. The process is repeated with the next best test pattern, and is stopped only when the test set becomes empty. An algorithm to implement SI-TNNC method is given in the paper which has a quadratic time complexity. Other related solutions have either cubic time complexity or are NP-hard. Experimentally, using several standard data-sets, it is shown that the proposed transductive learner achieves on-par or better classification accuracy than its related competitors.

Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants’ credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive performance of credit scoring model. Towards to feature selection, Binary BAT optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with “Radial Basis Function Neural Network (RBFN)”, “Support Vector Machine (SVM)” and “Random Forest (RF)” for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring.