A New Kind of Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Platform

2014 ◽  
Vol 644-650 ◽  
pp. 2018-2021 ◽  
Author(s):  
Bin Ma
Keyword(s):  
Public Opinion ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
Classification Method ◽  
Hadoop Platform ◽  
Classification Test ◽  
Network Public Opinion

According to the network public opinion’s characteristics of distributed, massive and heterogeneous, a new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop plateform is studied. The classification ability and execution efficiency of proposed scheme is verified and applied to the network public opinion documents classification test. The results show that the parallel K_NN algorithm can achieve rapid and accurate classification of network public opinion.

Study of a New Parallel K_NN Network Public Opinion Classification Algorithm Based on Hadoop Environment

2014 ◽  
Vol 635-637 ◽  
pp. 1624-1627
Author(s):  
Jian Xu ◽  
Bin Ma
Keyword(s):  
Public Opinion ◽  
Parallel Processing ◽  
Nearest Neighbor ◽  
Distributed Storage ◽  
Classification Algorithm ◽  
Classification Method ◽  
Hadoop Platform ◽  
Network Public Opinion

A new kind of network public opinion classification method based on K_ nearest neighbor (K_NN) classification algorithm in Hadoop environment is studied in this paper. In the light of distributed storage and parallel processing Characteristics of Hadoop platform, the parallel K_NN classification algorithm in the frame of MapReduce is designed. The classification ability and execution efficiency of proposed scheme is verified and the results show that the parallel K_NN algorithm enhances the network public opinion classification precision and execution efficiently.

Study of Network Public Opinion Classification Method Based on Naive Bayesian Algorithm in Hadoop Environment

2014 ◽  
Vol 519-520 ◽  
pp. 58-61 ◽  
Author(s):  
Jian Xu ◽  
Bin Ma
Keyword(s):  
Public Opinion ◽  
Distributed Storage ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Classification Method ◽  
Single Server ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Bayes Algorithm ◽  
Network Public Opinion

In the light of the excellent distributed storage and parallel processing feature of hadoop cluster, a new kind of network public opinion classification method based on Naive Bayes algorithm in hadoop environment is studied. The collected public opinion documents are stored locally according to the HDFS architecture, and whose character words are extracted paralleled in Mapreduce process. Thus the naive Bayesian classification algorithm is parallel encapsulated on cloud computing platform. The MapReduce packaged Naive Bayesian classification algorithm performance is verified and the results show that the algorithm execution speed are significantly improved compared to a single server. Its public opinion classification accuracy rate is more than 85%, which can effectively improve the classification performance of network public opinion and classification efficiency.

scholarly journals A High Performace of Local Binary Pattern on Classify Javanese Character Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 8 ◽  
Author(s):  
Ajib Susanto ◽  
Daurat Sinaga ◽  
Christy Atika Sari ◽  
Eko Hari Rachmawanto ◽  
De Rosal Ignatius Moses Setiadi
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Local Binary Pattern ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
K Nearest Neighbor ◽  
Characteristic Extraction ◽  
Research Objects ◽  
Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

scholarly journals Nonuniform Granularity-Based Classification in Social Interest Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Wenjuan Shao ◽  
Qingguo Shen ◽  
Xianli Jin ◽  
Liaoruo Huang ◽  
Jingjing Chen
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Social Interest ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Computing Paradigm ◽  
The Social ◽  
Interest Detection

Social interest detection is a new computing paradigm which processes a great variety of large scale resources. Effective classification of these resources is necessary for the social interest detection. In this paper, we describe some concepts and principles about classification and present a novel classification algorithm based on nonuniform granularity. Clustering algorithm is used to generate a clustering pedigree chart. By using suitable classification cutting values to cut the chart, we can get different branches which are used as categories. The size of cutting value is vital to the performance and can be dynamically adapted in the proposed algorithm. Experiments results carried on the blog posts illustrate the effectiveness of the proposed algorithm. Furthermore, the results for comparing with Naive Bayes, k-nearest neighbor, and so forth validate the better classification performance of the proposed algorithm for large scale resources.

scholarly journals The Classification of Public Complaints through Smart City Application

2020 ◽  
Author(s):  
Evaristus Didik Madyatmadja ◽  
Cristofer Wijaya
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
Support Vector ◽  
Search Process ◽  
Linear Kernel ◽  
K Nearest Neighbor ◽  
Mining Methods ◽  
Algorithm Search

Abstract This research aimed to classify the data of public complaints of people in Tangerang City by applying a pattern of the complaint data from the LAKSA application that has been categorized. In finding the pattern, it used one of the data mining methods, namely classification. The classification algorithm search process was performed by comparing the accuracy of several selected algorithms. The algorithms were k-nearest neighbor, random forest, support vector machine, and AdaBoost. These algorithms were tested to achieve maximum potential. Thus, the results showed support vector machine with linear kernel is a classification algorithm with the highest accuracy that reached 89.2%

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

Sign in / Sign up

Export Citation Format

Share Document