RESUMEN
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it's important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD.
Asunto(s)
Trastorno Bipolar , Trastorno de Personalidad Limítrofe , Electroencefalografía , Aprendizaje Automático , Humanos , Trastorno de Personalidad Limítrofe/diagnóstico , Trastorno de Personalidad Limítrofe/fisiopatología , Trastorno Bipolar/diagnóstico , Trastorno Bipolar/fisiopatología , Electroencefalografía/métodos , Adulto , Femenino , Masculino , Diagnóstico Diferencial , Adulto Joven , Cognición/fisiología , AlgoritmosRESUMEN
Answer selection (AS) is a critical subtask of the open-domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedding, enriched by an improved artificial bee colony (ABC) algorithm for pretraining and a reinforcement learning-based algorithm for training backpropagation (BP) algorithm. BERT can be comprised in downstream work and fine-tuned as a united task-specific architecture, and the pretrained BERT model can grab different linguistic effects. Existing algorithms typically train the AS model with positive-negative pairs for a two-class classifier. A positive pair contains a question and a genuine answer, while a negative one includes a question and a fake answer. The output should be one for positive and zero for negative pairs. Typically, negative pairs are more than positive, leading to an imbalanced classification that drastically reduces system performance. To deal with it, we define classification as a sequential decision-making process in which the agent takes a sample at each step and classifies it. For each classification operation, the agent receives a reward, in which the prize of the majority class is less than the reward of the minority class. Ultimately, the agent finds the optimal value for the policy weights. We initialize the policy weights with the improved ABC algorithm. The initial value technique can prevent problems such as getting stuck in the local optimum. Although ABC serves well in most tasks, there is still a weakness in the ABC algorithm that disregards the fitness of related pairs of individuals in discovering a neighboring food source position. Therefore, this paper also proposes a mutual learning technique that modifies the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. We tested our model on three datasets, LegalQA, TrecQA, and WikiQA, and the results show that RLAS-BIABC can be recognized as a state-of-the-art method.
Asunto(s)
Algoritmos , Refuerzo en Psicología , AprendizajeRESUMEN
OBJECTIVE: In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner. APPROACH: The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples. MAIN RESULTS: We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique-leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data. SIGNIFICANCE: The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain-computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.