A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.Comment: 32 pages, 12 figures, 7 table

Implementation of K-NN Algorithm to classify the Scholarship Recipients of Aceh Carong at Universitas Malikussaleh

In an effort to increase the efficiency of the scholarship selection process, this research aims to implement the K-Nearest Neighbors (K-NN) algorithm in the classification of scholarship recipients. The research method involves collecting data on scholarship receipts from several previous years based on predetermined criteria such as father's job, mother's job, parent's income, number of parents working, father's last education, and mother's last education. Next, the K-NN algorithm is applied to classify prospective scholarship recipients based on the similarity of their profiles to students who have received previous scholarships. The results of this research indicate that the implementation of the K-NN algorithm in the classification of scholarship admissions at Malikussaleh Aceh Carong University can increase selection accuracy. The experimental results of the accuracy values obtained show that using the K-Nearest Neighbors algorithm with the Euclidean Distance approach and a value of K = 3 produces an algorithm accuracy level of 87.55%. Thus, the K-NN algorithm can be a useful method for scholarship selectors to support more precise and objective decision making

A new genetic algorithm for multi-label correlation-based feature selection.

This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi-label classification algorithm - in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the previously proposed Hill-Climbing for Multi-Label Correlation-Based Feature Selection (HC-ML-CFS), across 10 multi-label datasets

RANCANG BANGUN SISTEM DETEKSI KEMATANGAN BUAH KELAPA SAWIT BERDASARKAN DETEKSI WARNA MENGGUNAKAN ALGORITMA K-NN

The rapid growth of the palm oil industry has made it increasingly important to develop applications that can detect the maturity level of oil palm fruit. This paper presents the design and development of an application for detecting the maturity level of oil palm fruit based on color composition using the K-NN algorithm. The K-NN algorithm is used to classify the oil palm fruit based on the color composition that is related to its maturity level.   The application uses image processing technology to measure the qualitative and quantitative parameters of various maturity indicators, such as color, size, and texture. Different color compositions of the oil palm fruit indicate different maturity levels, and using the K-NN algorithm, the fruit can be classified based on its maturity level. The application helps reduce production costs and losses caused by errors in harvesting the fruit.   The application is designed to be user-friendly and accessible to farmers and plantation managers. The user interface is simple and intuitive, allowing users to easily input the image of the oil palm fruit and get a quick analysis of its maturity level. The results are displayed in a clear and understandable way, making it easy for users to make informed decisions about when to harvest the fruit.   In conclusion, the application for detecting the maturity level of oil palm fruit based on color composition using the K-NN algorithm is a useful tool in the palm oil industry. It helps farmers and plantation managers determine the optimal time for harvesting the fruit, reducing production costs and increasing productivity. The user-friendly interface makes it accessible to a wider range of users and facilitates informed decision-maki

IMPLEMENTATION OF DECISION TREE AND K-NN CLASSIFICATION OF INTEREST IN CONTINUING STUDENT SCHOOL

Education is important to prepare quality Human Resources (HR) because quality human resources is an important factor for the nation and state development. Therefore, it is expected that every citizen has the right to get high educational opportunities from the 12-year compulsory education level. This study aims to implement the Decision Tree and K-NN algorithm in the classification of student interest in continuing school.  This study proposes combining the Decision Tree and K-NN algorithm methods to improve accuracy with the Gain Ratio, Information Gain and Gini Index approaches for the measurement process. The test results show that the use of the Decision Tree algorithm produces an accuracy value of 97.30% while using the K-NN algorithm produces an accuracy of 89.60%. While the proposed method by combining the Decision Tree and K-NN algorithms produces an accuracy value of 98.07%. The results of evaluation measurements using the Area Under Curve (AUC) on the Decision Tree algorithm are 0.992 and the AUC on K-NN is 0.958 and on the combination of the Decision Tree and K-NN algorithms of 0.979. These results indicate that the proposed algorithm is very significant towards increasing accuracy in the classification of the interests of high school students continuing schoo

Klasifikasi Sentimen Pembeli Berdasarkan Layanan SMS “Suara Konsumen” Terhadap Produk Menggunakan Metode K-nn

Limited number of characters in an SMS causes the use of words and the structure of solid and compact but obviously become very important in presenting the content and purpose of the sender of the SMS. This research studied how the use of SMS classify structures and limited words to get the sentiment of an SMS service "Suara Konsumen" through text mining approach. This study uses K-NN algorithm in the classification, besides the K-NN algorithm of this study will be weighted words using TF-IDF methods, Feature Selection in the selection of words also Cosine Similiarity in measuring the degree of proximity between documents. In determining the success, measured the accuracy of the documents the trial and the results are very accurate with an average value of 89% accuracy on the variation of K and Feature Selection