Improved K-nearest Neighbor Algorithm Research Articles

Predicting Trust Relationships in Social Networks Based on WKNN

Trust relationships between user pairs play a vital role in making decisions for social network users. In reality, available explicit trust relations are often extremely sparse, therefore, inferring unknown trust relations attracts increasing attention in recent years. In this paper, a new approach originating from machine learning is proposed to predict trust relationships in social networks by exploring an improved k-nearest neighbor algorithm based on distance weight (WKNN). Firstly, we extract three critical attributes from users' personal profiles and interactive information; then, an improved KNN algorithm named WKNN is proposed; finally, comparative analysis between them is performed by using real-world dataset from Epinions to evaluate their performance in trust prediction. Empirical evaluation demonstrates that the proposed framework (WKNN model) is feasible and effective in predicting trust relationships.

Affection Recognition by GSR Signal Use a Improved K-nearest Neighbor Algorithm

For afiection recognition problems of GSR (Galvanic Skin Response) signal, this paper presents a method of afiection recognition based on a improved K-nearest neighbor algorithm. In this research, on the flrst step, the group collected 175 participants’ GSR signals on the test of emotional arousal which was acquired in the lab environment; then used the Batterworth low pass fllter to remove GSR signals noise, and extracted the original features of GSR signals at the same time; then recognized the flve feelings of happiness, sadness, fear, anger and calm with the improved K- nearest neighbor classifler. The experimental results show that the method can improves the accuracy of identiflcation indeed.

3D CAD Model Retrieval Based on Feature Fusion

Existing 3D model retrieval techniques focus on the global feature more than local detailed feature are not applicable to 3D CAD models. This paper combines the local feature and global feature to satisfy the needs of mechanical design. We first analyze the limitations of Shape Description (SD) algorithm. Then propose an automatic feature extraction technology based on the local characteristics of the curvature distribution. Next we fuse the extracted feature using a novel method. Lastly, we design an improved K-nearest neighbor algorithm to retrieve models. Experimental results indicate the efficiency and feasibility of the proposed method.

Modeling of Gan Hemt by Using an Improved K-Nearest Neighbors Algorithm

A novel black-box modeling method for field effect transistor (FET) based on an improved K-Nearest Neighbors algorithm is proposed in this paper. K-Nearest Neighbors algorithm, which has simple algorithm structure and high accuracy, has shown great potential in regression application. A Taylor series expansion method is employed in nonlinear elements modeling of FET to improve the physical meaning of black-box model. A GaN HEMT power device is used to demonstrate the proposed method. And the experimentation shows that the calculated results using improved K-Nearest Neighbors algorithm based model fit the measurement results well.