scholarly journals Ensembles of Classification Methods for Data Mining Applications

2013 ◽  
Vol 5 (6) ◽  
pp. 6-21
Author(s):  
M. Govindarajan
Keyword(s):  
Data Mining ◽  
Classification Methods

A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

2017 ◽  
Vol 3 (1) ◽  
pp. 76 ◽  
Author(s):  
Duong Tran Duc ◽  
Pham Bao Son ◽  
Tan Hanh ◽  
Le Truong Thien
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Web Applications ◽  
Service Providers ◽  
Main Idea ◽  
Classification Methods ◽  
Cost Sensitive Learning ◽  
Network Method ◽  
Demographic Attributes ◽  
Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

scholarly journals Integration of synthetic minority oversampling technique for imbalanced class

2019 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Noviyanti Santoso ◽  
Wahyu Wibowo ◽  
Hilda Hikmawati
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Original Data ◽  
Support Vector ◽  
Classification Methods ◽  
Problematic Issue ◽  
Imbalanced Class ◽  
F Measure

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

scholarly journals Prediction Model on Student Performance based on Internal Assessment using Deep Learning

2019 ◽  
Vol 14 (08) ◽  
pp. 4 ◽  
Author(s):  
Sadiq Hussain ◽  
Zahraa Fadhil Muhsion ◽  
Yass Khudheir Salal ◽  
Paraskevi Theodoru ◽  
Fikriye Kurtoğlu ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Student Performance ◽  
Recommendation System ◽  
Optimization Method ◽  
Recognition System ◽  
Kappa Statistics ◽  
Immune Recognition ◽  
Classification Methods ◽  
Classification Rate

Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.

Classification Methods in the Detection of New Suspicious Emails

2008 ◽  
Vol 07 (03) ◽  
pp. 209-217 ◽  
Author(s):  
S. Appavu Alias Balamurugan ◽  
G. Athiappan ◽  
M. Muthu Pandian ◽  
R. Rajaram
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Binary Tree ◽  
Experimental Results ◽  
Classification Methods ◽  
Detection Rates ◽  
Naive Bayesian ◽  
The Past ◽  
Naïve Bayesian

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

Prediction of Selected Production Goals by Classification Methods

2014 ◽  
Vol 474 ◽  
pp. 115-120 ◽  
Author(s):  
Dominika Jurovatá ◽  
Pavel Važan ◽  
Michal Kebisek ◽  
Pavol Tanuska ◽  
Lukáš Hrčka
Keyword(s):  
Data Mining ◽  
Production Process ◽  
Knowledge Discovery ◽  
Predictive Models ◽  
Classification Methods ◽  
System Behavior ◽  
Planning And Control ◽  
Input Parameters ◽  
Process Behavior ◽  
And Control

The goal of this work was to use the process of knowledge discovery in planning and control of production processes. This work is focused on the prediction of the system behavior from the data of production process. The classification was used as a task of data mining. Some predictive models were created and the predictions of the production process behavior were realized by varying the input parameters using selected methods and techniques of data mining. It can be confirmed that the selected input parameters will lead to the fulfillment of the declared objectives of the process. The process of knowledge discovery has been implemented in the program STATISTICA Data Miner.

Application of Data Mining Technology in CRM

2014 ◽  
Vol 513-517 ◽  
pp. 2103-2106
Author(s):  
Ya Fei Liu ◽  
Meng Ya Li
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Methods ◽  
Decision Tree Algorithm ◽  
Core Competitiveness ◽  
Mining Technology ◽  
The Core ◽  
The Core Competitiveness ◽  
Mining Model ◽  
Potential Customers

Regarding to the technical difficulties of reachingvaluable knowledge and information from CRM mass of data and information, this article brings up the idea that we could apply the data mining technology in CRM. The data mining classification methods helps us to find out the potential customers with high value. The article describe how the methods and processes how could we apply the data mining technology to CRM and on the basis of decision tree algorithm to construct the CRM data mining model, and help companies better understand the customers behaviors and improve the core competitiveness of enterprises.

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