Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

The problem of imbalanced data classification learning has received much attention.Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples.Majority weighted minority oversampling technique(MWMOTE)is an effective approach to solve this problem,however,it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data.To this end,we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering(SPMSC)to solve these problems.Firstly,in order to effectively eliminate the effect of noisy samples,SPMsC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample.Note that MWMOTE may generate duplicate samples,we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples.Finally,data cleaning is performed on the processed data to further eliminate class overlap.Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMsC compared with other sampling methods.

Trigger and noise filtering algorithms for the LHAASO water Cherenkov detector array

Introduction The Large High Altitude Air Shower Observatory(LHAASO)will be constructed at Mt.Haizishan,Sichuan Province,China.Among several detector components of the LHAASO,the Water Cherenkov Detector Array(WCDA)is of great importance for gamma-ray astronomy from 100 GeV to 30 TeV.The single-channel counting rate of a photo-multiplier tube can reach as high as 30-35 kHz,most of them are background noise hits from the low energy cosmic ray showers,bringing a big challenge on data transferring,data storage and event reconstruction.Methods In this paper,a dedicated trigger scheme and a fast noise filtering method aiming to deal with these high rate background noise hits are introduced.These methods are tested with some Monte Carlo simulation data,showing a fair efficiency in filtering background noise hits,while most of the real shower signals are kept.Conclusion This method is proposed to be applied in a stage of the online processing just after the data are acquired in LHAASO-WCDA.

An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease

The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease.Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases.Among many existing methods,a few have considered the class imbalance issues of liver disorder datasets.As all the samples of liver disorder datasets are not useful,they do not contribute to learning about classifiers.A few samples might be redundant,which can increase the computational cost and affect the performance of the classifier.In this paper,a model has been proposed that combines noise filter,fuzzy sets,and boosting techniques(NFFBTs)for liver disease prediction.Firstly,the noise filter(NF)eliminates the outliers from the minority class and removes the outlier and redundant pair from the majority class.Secondly,the fuzzy set concept is applied to handle uncertainty in datasets.Thirdly,the AdaBoost boosting algorithm is trained with several learners viz,random forest(RF),support vector machine(SVM),logistic regression(LR),and naive Bayes(NB).The proposed NFFBT prediction system was applied to two datasets(i.e.,ILPD and MPRLPD)and found that AdaBoost with RF yielded 90.65%and 98.95%accuracy and F1 scores of 92.09%and 99.24%over ILPD and MPRLPD datasets,respectively.

Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.

Applying nonlinear filtering in reverberation time measurement under strong background noise

Nonlinear filtering of impulse response obtained by M-sequence correlation method under strong background noise is presented. The research shows that the new method works very efficiently without the need to cut off impulse response data. Even if the ratio of signal to noise is below -15 dB, the same decay curve ranges can still be obtained as when S/N > 40

Mode shape description of an aero-engine casing structure using Zernike moment descriptors

Vibration mode shape description of an aero-engine casing structure using Zernike moment descriptor(ZMD) was introduced in this paper.The mode shapes of the aero-engine casing structure can be decomposed as a linear combination of a series of Zernike polynomials,with the feature of each Zernike polynomial reflecting a part of characteristic of mode shapes,based on Zernike moment transformation.Meanwhile,the reconstruction of mode shapes with ZMD was explored and its ability to filtering the noise contaminated in the mode shapes was studied.Simulation of the aero-engine casing structure indicated the advantage of this method to depict the mode shapes of a symmetric structure.Results demonstrate that the Zernike moment description of the mode shapes can effectively describe the double modes in the symmetric structure and also has the ability to remove or significantly reduce the influence of noise in the mode shapes.Such feature shows great practical value for further research on the correlation,model updating and model validation of the symmetric structure's finite element model.