Weighted k Nearest Neighbor Using Grey Relational Analysis To Solve Missing Value
نویسندگان
چکیده
منابع مشابه
Drought Monitoring and Prediction using K-Nearest Neighbor Algorithm
Drought is a climate phenomenon which might occur in any climate condition and all regions on the earth. Effective drought management depends on the application of appropriate drought indices. Drought indices are variables which are used to detect and characterize drought conditions. In this study, it was tried to predict drought occurrence, based on the standard precipitation index (SPI), usin...
متن کاملNeighbor-weighted K-nearest neighbor for unbalanced text corpus
Text categorization or classification is the automated assigning of text documents to pre-defined classes based on their contents. Many of classification algorithms usually assume that the training examples are evenly distributed among different classes. However, unbalanced data sets often appear in many practical applications. In order to deal with uneven text sets, we propose the neighbor-wei...
متن کاملk-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. Howeve...
متن کاملA Weighted k-Nearest Neighbor Density Estimate for Geometric Inference
Abstract Motivated by a broad range of potential applications in topological and geometric inference, we introduce a weighted version of the knearest neighbor density estimate. Various pointwise consistency results of this estimate are established. We present a general central limit theorem under the lightest possible conditions. In addition, a strong approximation result is obtained and the ch...
متن کاملWeighted k-Nearest-Neighbor Techniques for High Throughput Screening Data
The k-nearest neighbors (knn) is a simple but effective method of classification. In this paper we present an extended version of this technique for chemical compounds used in High Throughput Screening, where the distances of the nearest neighbors can be taken into account. Our algorithm uses kernel weight functions as guidance for the process of defining activity in screening data. Proposed ke...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IPTEK The Journal for Technology and Science
سال: 2018
ISSN: 2088-2033,0853-4098
DOI: 10.12962/j20882033.v29i3.5011