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Gaussian Naive Bayes (GNB) is a popular supervised learning algorithm to address various classification issues. GNB has strong theoretical basis, however, its performance tends to be hurt by skewed data distribution. In this study, we present an optimal decision threshold-moving strategy for helping GNB to adapt imbalanced classification data. Specifically, a PSO-based optimal procedure is conducted to tune the posterior probabilities produced by GNB, further repairing the bias on classification boundary. The proposed GNB-ODTM algorithm presents excellent adaptation to skewed data distribution. Experimental results on eight class imbalance data sets also indicate the effectiveness and superiority of the proposed algorithm.

Target radiation noise preprocessing is the basis and premise of underwater acoustic signal processing. In order to obtain better noise reduction effect, a method of underwater target radiation noise reduction based on superposition variational mode decomposition residual is proposed. The initial signal collected by passive sonar is decomposed into a series of intrinsic mode functions and residuals by variational mode decomposition, and the spatial-dependence recurrence sample entropy of each intrinsic mode function is calculated. According to this value, the intrinsic mode functions are divided into noise-dominated intrinsic mode function and signal-dominated intrinsic mode function. The noise-dominated intrinsic mode function is denoised by improved wavelet threshold denoising. While SG filter denoises the signaldominated intrinsic mode function, restructures the processed natural mode function, and superposes the residual from the variational mode decomposition to obtain the de-noised signal. The proposed method was applied to the experimental data of SWellEx-96, an open source data set. The results show that, the proposed method has certain advantages. The improved signal-to-noise ratio is higher than 55.4dB and the root-mean square error is less than 1.8*10-5, which makes the line spectrum characteristics in the frequency domain more obvious. Therefore, this method has certain practical application value.

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