scholarly journals Unique Ion Filter: A Data Reduction Tool for GC/MS Data Preprocessing Prior to Chemometric Analysis

2014 ◽  
Vol 86 (15) ◽  
pp. 7726-7733 ◽  
Author(s):  
L. A. Adutwum ◽  
J. J. Harynuk
Keyword(s):  
Data Reduction ◽  
Data Preprocessing ◽  
Chemometric Analysis ◽  
Ion Filter

scholarly journals Klasifikasi Malicious Websites Menggunakan Algoritma K-NN Berdasarkan Application Layers dan Network Characteristics

2018 ◽  
Vol 4 (1) ◽  
pp. 37
Author(s):  
Green Arther Sandag ◽  
Jonathan Leopold ◽  
Vinky Fransiscus Ong
Keyword(s):  
Feature Selection ◽  
Data Reduction ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Data Cleaning ◽  
Data Preprocessing ◽  
Naïve Bayes ◽  
Application Layer ◽  
K Nearest Neighbor ◽  
Network Characteristics

Dalam kehidupan di era teknologi sekarang ini semua aktivitas manusia telah dipengaruhi oleh internet. Berbagi informasi, komunikasi, sosialisasi, berbelanja, berbisnis, pendidikan dan banyak hal lainnya yang dapat dilakukan menggunakan internet. Seiring dengan berkembangnya internet berbagai macam ancaman keamanan menjadi lebih beragam. Virus adalah musuh nomor satu di internet. Virus memanfaatkan berbagai metode untuk dapat menghindari anti-virus, salah satunya adalah Malware. Malware adalah salah satu kode berbahaya yang dapat mengubah, merusak dan mencuri data pribadi yang dapat merugikan individual ataupun kelompok. Penelitian ini akan memprediksi malicious website berdasarkan application layer dan network characteristics menggunakan metode K-Nearest Neighbor. Penelitian ini menggunakan metode data cleaning dan data reduction untuk data preprocessing, dan feature selection untuk pemilihan attribut yang paling berpengaruh pada malicious website. Untuk memprediksi malicious website penulis menggunakan algoritma K-NN dengan hasil 2,42% precision lebih tinggi dibandingkan dengan penelitian sebelumnya yang menggunakan algoritma Naïve Bayes.  Keywords : Klasifikasi, Network Characteristics, Malicious Websites, Application Layers, K-NN, Naïve Bayes

Dimensionality and data reduction in telecom churn prediction

Kybernetes ◽  
2014 ◽  
Vol 43 (5) ◽  
pp. 737-749 ◽  
Author(s):  
Wei-Chao Lin ◽  
Chih-Fong Tsai ◽  
Shih-Wen Ke
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Data Reduction ◽  
Data Preprocessing ◽  
Churn Prediction ◽  
Important Data ◽  
Data Set ◽  
Content Type ◽  
Customer Churn

Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results. Design/methodology/approach – Based on a real telecom customer churn data set, seven different preprocessed data sets based on performing feature selection and data reduction by different priorities are used to train the artificial neural network as the churn prediction model. Findings – The results show that performing data reduction first by self-organizing maps and feature selection second by principal component analysis can allow the prediction model to provide the highest prediction accuracy. In addition, this priority allows the prediction model for more efficient learning since 66 and 62 percent of the original features and data samples are reduced, respectively. Originality/value – The contribution of this paper is to understand the better procedure of performing the two important data preprocessing steps for telecom churn prediction.

scholarly journals Data Classification Methodology for Electronic Noses Using Uniform Manifold Approximation and Projection and Extreme Learning Machine

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Jersson X. Leon-Medina ◽  
Núria Parés ◽  
Maribel Anaya ◽  
Diego A. Tibaduiza ◽  
Francesc Pozo
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Data Reduction ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Data Preprocessing ◽  
Electronic Noses ◽  
Average Classification Accuracy ◽  
Learning Classifier ◽  
Learning Machine

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known min−max approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy.

The CLASSIC/CLIMB Data Reduction: The Math

2014 ◽  
Vol 69-70 ◽  
pp. 75-99
Author(s):  
T. ten Brummelaar
Keyword(s):  
Data Reduction

ELECTRONIC AREA DETECTOR DATA REDUCTION SYSTEM AT THE SRS

1986 ◽  
Vol 47 (C5) ◽  
pp. C5-109-C5-113
Author(s):  
J. W. CAMPBELL ◽  
D. CROFT ◽  
J. R. HELLIWELL ◽  
P. MACHIN ◽  
M. Z. PAPIZ ◽  
...  
Keyword(s):  
Data Reduction ◽  
Area Detector ◽  
Reduction System

Differentiation of Flos Chrysanthemi Cultivars by NMR Fingerprinting and Chemometric Analysis

Planta Medica ◽  
2012 ◽  
Vol 78 (05) ◽  
Author(s):  
J Zhao ◽  
M Wang ◽  
F Wei ◽  
R Lin ◽  
TJ Smillie ◽  
...  
Keyword(s):  
Chemometric Analysis

Chemometric analysis on Acanthopanax fruits using UPLC-ESI-MS

Planta Medica ◽  
2013 ◽  
Vol 79 (13) ◽  
Author(s):  
S Jeong ◽  
Y Jang
Keyword(s):  
Chemometric Analysis ◽  
Esi Ms

INOVASI PEMBELAJARAN AKIDAH AKHLAK MENGGUNAKAN METODE ROLE PLAY DALAM MENINGKATKAN PRESTASI BELAJAR SISWA

2018 ◽  
Vol 2 (1) ◽  
pp. 52-63
Author(s):  
Ansori Ansori
Keyword(s):  
Data Reduction ◽  
Role Play ◽  
Learning Goals ◽  
New Paradigm ◽  
Learning Materials ◽  
Analysis Technique ◽  
Flexible Schedule ◽  
Using Data ◽  
Real Action

The use of various methods will greatly help students in achieving learning goals. As role play method is one way mastery of learning materials through the development of imagination and appreciation of students on learning materials. Data collection techniques in this study are observation, interviews, and documentation. To analyze the data in this research using data analysis technique of Miles and Huberman model that is data reduction (Data Reduction), data presentation (Data Display) and conclusion (Conclution Drawing / verification) The findings in this research is innovation of role play method can change paradigm to the new paradigm so that the role of the teacher is more as a facilitator, counselor, consultant, and comrade study Flexible schedule, open as needed Learning directed by students themselves Problem-based, project, real world, real action, and reflection Design and investigation. Computers as tools, and dynamic media presentations.

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