Predictive Models of Economic Systems Based on Data Mining

2015 ◽  
Author(s):  
Jose Cazal
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Economic Systems

Proposal of Analytical Model for Business Problems Solving in Big Data Environment

Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Predictive Models ◽  
Analytical Approach ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Data Sources ◽  
Business Decisions ◽  
Mining Projects ◽  
Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

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.

The future of predictive models in radiation oncology: from extensive data mining to reliable modeling of the results

2013 ◽  
Vol 9 (3) ◽  
pp. 311-313 ◽  
Author(s):  
Vincenzo Valentini ◽  
Nicola Dinapoli ◽  
Andrea Damiani
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Radiation Oncology ◽  
Extensive Data ◽  
The Future

scholarly journals Enhancing Generalizability of Predictive Models with Synergy of Data and Physics

2021 ◽  
Author(s):  
Yingjun Shen ◽  
Zhe Song ◽  
Andrew Kusiak
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Predictive Models ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Divide And Conquer ◽  
Less Is More ◽  
Industrial Big Data

Abstract Wind farm needs prediction models for predictive maintenance. There is a need to predict values of non-observable parameters beyond ranges reflected in available data. A prediction model developed for one machine many not perform well in another similar machine. This is usually due to lack of generalizability of data-driven models. To increase generalizability of predictive models, this research integrates the data mining with first-principle knowledge. Physics-based principles are combined with machine learning algorithms through feature engineering, strong rules and divide-and-conquer. The proposed synergy concept is illustrated with the wind turbine blade icing prediction and achieves significant prediction accuracy across different turbines. The proposed process is widely accepted by wind energy predictive maintenance practitioners because of its simplicity and efficiency. Furthermore, the testing scores of KNN, CART and DNN algorithm are increased by 44.78%, 32.72% and 9.13% with our proposed process. We demonstrated the importance of embedding physical principles within the machine learning process, and also highlight an important point that the need for more complex machine learning algorithms in industrial big data mining is often much less than it is in other applications, making it essential to incorporate physics and follow “Less is More” philosophy.

scholarly journals Collaborating All the Way to the Top

2014 ◽  
Vol 6 ◽  
Author(s):  
Huzefa Rangwala
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Structure Prediction ◽  
Predictive Performance ◽  
General Purpose ◽  
Sequence Information ◽  
Base Line ◽  
Specific Class ◽  
Truth Values ◽  
Final Project

The classes I teach have a predictive modeling component. As a student, having participated in blind protein structure prediction competitions (CASP, http://predictioncenter.org) and data mining competitions like KDD Cup, I have implemented this form of competitions in my bioinformatics and data mining classes. This semester I extended this idea to a different class (Parallel Computing). Specifically, as part of an assignment (or final project) students have to train a predictive models to distinguish a specific class of proteins called "solenoids" using the available protein sequence information. As part of this competition, the truth-values are hidden from the students and they have to make a prediction (guess) and submit their results to the instructor. The instructor then evaluates the results using the truth-values and provides a ranking of the class students based on the predictive performance. The concepts introduced in class allow the students to build base line predictive models, but to improve performance, students have to research, think critically and come up with innovative solutions. In my past two implementations of this project, I have used an in-house evaluation script and requested participants to send me solutions via a simple web server. Both times, the assignment was run for a 4-week period. I have also used technologies like Kaggle to setup this competition. In the future, I would like to implement the competition for the duration of the semester. Students would be taught a concept, and they would implement the same towards improving their predictive models and engineer better solutions as new, advanced concepts are taught.I am also developing a model that allows students to achieve these projects in a collaborative fashion by enabling resources like Wikis and other tools. As such, this session will be an introduction to the tools used and how they could be adapted to general purpose classes

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