درخت حوزه‌های تخصصی

K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algorithm does not consider the differences between samples, which led the algorithm to have inaccurate predictions. In this paper, we proposed a novel scheme for improving the accuracy of the KNN classification algorithm based on the new weighting technique and stepwise feature selection. First, we used a stepwise feature selection method to eliminate irrelevant features and select highly correlated features with the class category. Then a new weighting method was proposed to give authority value to each sample in train dataset based on neighbor categories and Euclidean distances. This weighting approach gives a higher preference to samples that have neighbors with close Euclidean distance while they are in the same category, which can effectively increase the classification accuracy of the algorithm. We evaluated the accuracy rate of the proposed method and analyzed it with the traditional KNN algorithm and some similar works with the use of five real-world UCI datasets. The experiment results determined that the proposed scheme (denoted by WAD-KNN) performed better than the traditional KNN algorithm and considered approaches with the improvement of approximately 10% accuracy.

In today's competitive world, competition from the level of companies is drawn to the competition between their supply chain. The purpose of this study was to design and validate a suitable model for identifying and ranking effective factors and key indicators related to the performance of the organization in the supply chain of Saipa Company . The research method is objective and applied in terms of method, descriptive and correlation. The research tool was in the qualitative section, interview and in the quantitative section, a questionnaire. Data analysis in the qualitative section was used to identify the dimensions of the variables with the help of the industry's elite and through the analysis of the fuzzy Delphi, which ultimately resulted in 179 components in four factors: " T echnology C apability", " I nnovation C apability", " C ompetitive A dvantage" and " Organizational P erformance" is identified. Fuzzy hierarchy analysis was used to rank the identified factors . In the quantitative section, structural equations were used to fit the model. In this regard, after reviewing theoretical literature and drawing up the original model, the research questionnaire was distributed along with the dimensions of 350 experts and managers of the Saipa group and supply chain. The reliability of the questionnaire was confirmed by factor load method, Cronbach's A lpha and composite reliability coefficient (CR) and its validity was confirmed by convergent validity (AVE) and divergent validity. To evaluate the structural model, three coefficients of determination (R 2 ), index (Q 2 ) and statistics (GOF) have been used . The dimensions of each of the variables and the prioritization of the dimensions of the variables were determined. The results were analyzed using Structural Equation Modeling (SEM) and Software (SPLS) and the relationship between the factors was obtained and the proposed model was confirmed using factor analysis and structural equation modeling techniques . According to the results of the verifiable analysis of this research model, it was found that the performance of the organization depends on the dimensions of " I nnovation C apability", " T echnology C apability" and " C ompetitive A dvantage" in the supply chain, and has a positive and significant effect .