One of the most readily available k NN implementations can be found in Weka[26]. The main function of interest is IBk,which is basically Algorithm8.1.However,IBk also allows you to specify a couple of choices of distance weighting and the option to determine a value of k by using cross-...
To get you on board, it’s worth taking a step back and doing a quick survey of machine learning in general. In this section, you’ll get an introduction to the fundamental idea behind machine learning, and you’ll see how the kNN algorithm relates to other machine learning tools. The ...
A mechanism that is based on the concept of nearest neighbor and where k is some constant represented by a certain number in a particular context, with the algorithm embodying certain useful features such as the use of input to predict output data points, has an application to problems of va...
In this section, we introduced a novel cost-efficient underwater sensor node localization mechanism based on the KNN algorithm. Supposed that All sensor nodes are deployed at a depth of 7 meters, tasked with predicting various underwater environmental parameters as shown in eq. (1), including wa...
Risk factors of the desired diseases were calculated and machine learning algorithm applied to provide the prediction of the diseases. Health monitoring is an economic discipline that focuses on the effective allocation of medical resources, mainly to maximize the benefits of society to hea...
[25] devised the high-dimensional kNNJoin+algorithm to dynamically update new data points, enabling incremental updates on kNN join results. But because it was a disk-based technique, it could not meet the real-time needs of real-world applications. Further work by Yang et al. [26] ...
In the second stage of the algorithm the selected models are further evaluated using the Brier score as a performance measure. The Brier score measures the difference between the observed state of the outcomes of the test instances and the estimated probabilities that are in turn used to classify...
Fast and Scalable kNN Search Algorithm The proposed computational chunking is presented as an algorithm in Figure 6 (Algorithm 2). It starts with an input matrix (or a chunk of the input matrix, discussed later) In and produces a kNN graph (Gk). First, the weight attr...
And the computational complexity of KNN cannot be overlooked, as it plays a major role in the entire clustering algorithm. FaceMap [35] introduces Map Equation [24] into face clustering tasks to perform unsupervised large-scale face clustering. However, the Map Equation requires a lot of memory...
To solve the problems, in the paper we propose a privacy-preserving kNN query processing algorithm via secure two-party computation on the encrypted database. Our algorithm preserves both data privacy and query privacy while hid- ing data access patterns. For this, we propose efficient and ...