Failure Detection Based on Anomaly Detection and Multiple-Layer Perceptron Facing Unbalanced Sample Set

As computational performance continues to improve, machine learning is increasingly being used in a variety of areas. Classification problems are one of the most common problems people encounter in their daily lives. However, many classification tasks are confronted with the problem of sample imbalance, which is considered tricky. Although researchers have developed many algorithms for this, problems, such as overfitting, still result in poor classification results in many cases. This paper tries to solve a binary classification with unbalanced sample set applying an idea of combining ready-made anomaly detection and deep learning methods, where anomaly detection algorithms are taken as filters to exclude the effect of samples that are easy to be recognized as the ones from the major category on the final classification done by neural network. This idea is proved more useful on the machine failure detection than using anomaly detection or MLP classifier alone and is believed to be able to serve as a backup or pretest choice in some classification tasks with sample imbalance

Diagnosis of Dementia and Alzheimer's Disease Based on Classification Algorithms

Alzheimer's disease is currently the most common kind of senile dementia. With the increasing aging degree of the global society, Alzheimer's disease will become an unavoidable social problem in an aging society. In order to improve this situation, artificial intelligence algorithms that are good at mining the internal laws of data are applied in the hope of more effectively diagnose this disease, which should be intervened as early as possible. After briefly restating the current situation of dementia and Alzheimer's disease, the diagnostic model for dementia is built using logistic regression, which achieves great accuracy despite the simplicity of the model. Then, two diagnostic models that can identify if the patient with dementia has Alzheimer's disease based on SVM and Random Forest are tested. Although both the algorithms perform poorly because of the sample imbalance, after processing the original data with SMOTE, their performances are largely improved