scholarly journals A Theoretical Analysis of Why Hybrid Ensembles Work

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Kuo-Wei Hsu
Keyword(s):  
Theoretical Analysis ◽  
Theoretical Foundation ◽  
Group Decision ◽  
Classification Performance ◽  
Decision Making Process ◽  
Classification Algorithms ◽  
Data Mining Algorithm ◽  
Ensembles Of Classifiers ◽  
Mining Algorithm ◽  
Different Types

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

Team Dynamics at Suvasi Textiles

2019 ◽  
pp. 1-6
Author(s):  
Kirti Sharda
Keyword(s):  
Decision Making ◽  
Theoretical Foundation ◽  
Group Decision ◽  
Personality Types ◽  
Decision Making Process ◽  
Myers Briggs Type Indicator ◽  
Conflict Negotiation ◽  
Type Indicator ◽  
Response To Change ◽  
The Impact

This case is intended to introduce participants to basic personality types and the role they play in group decision making situations. The Myers-Briggs Type Indicator (MBTI) framework is used as the theoretical foundation to explore various personality types. The complexities involved in decision making in a group with divergent personalities are explored through a dilemma faced by Suvasi Textiles. The management team of Suvasi Textiles is grappling with a critical decision which can decisively change the future of the organisation; the case illustrates how the different personality types are impacting the decision making process. The case can serve to highlight the impact of type dynamics on team effectiveness, conflict negotiation, response to change and stress management.

scholarly journals Research of the Optimization of a Data Mining Algorithm Based on an Embedded Data Mining System

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 5-17
Author(s):  
Xindi Wang ◽  
Mengfei Chen ◽  
Li Chen
Keyword(s):  
Data Mining ◽  
Theoretical Analysis ◽  
Strong Association ◽  
Database System ◽  
Data Mining Algorithm ◽  
Mining System ◽  
Mining Algorithm ◽  
Binary Coding ◽  
Data Mining System ◽  
Frequent Item Sets

Abstract At present most of the data mining systems are independent with respect to the database system, and data loading and conversion take much time. The running time of the algorithms in a data mining process is also long. Although some optimized algorithms have improved it in different aspects, they could not improve the efficiency to a large extent when many duplicate records are available in a database. Solving the problem of improving the efficiency of data mining in the presence of many coinciding records in a database, an Apriori optimized algorithm is proposed. Firstly, a new concept of duplication and use is suggested to remove and count the same records, in order to generate a new database of a small size. Secondly, the original database is compressed according to the users’ requirements. At last, finding the frequent item sets based on binary coding, strong association rules are obtained. The structure of the data mining system based on an embedded database has also been designed in this paper. The theoretical analysis and experimental verification prove that the optimized algorithm is appropriate and the algorithm application in an embedded data mining system can further improve the mining efficiency.

scholarly journals Classification Performance Using Principal Component Analysis and Different Value of the Ratio R

2011 ◽  
Vol 6 (2) ◽  
pp. 317 ◽  
Author(s):  
Jasmina Novakovic ◽  
Sinisa Rankov
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Data Sets ◽  
Best Value ◽  
Inherent Feature ◽  
Different Types

A comparison between several classification algorithms with feature extraction on real dataset is presented. Principal Component Analysis (PCA) has been used for feature extraction with different values of the ratio R, evaluated and compared using four different types of classifiers on two real benchmark data sets. Accuracy of the classifiers is influenced by the choice of different values of the ratio R. There is no best value of the ratio R, for different datasets and different classifiers accuracy curves as a function of the number of features used may significantly differ. In our cases feature extraction is especially effective for classification algorithms that do not have any inherent feature selections or feature extraction build in, such as the nearest neighbour methods or some types of neural networks.

scholarly journals An Automatic Classification Method of Sports Teaching Video Using Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhang Min-qing ◽  
Li Wen-ping
Keyword(s):  
Support Vector Machine ◽  
Video Segmentation ◽  
Prediction Method ◽  
Classification Performance ◽  
Video Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Video Classification ◽  
Video Feature ◽  
Different Types

There are many different types of sports training films, and categorizing them can be difficult. As a result, this research introduces an autonomous video content classification system that makes managing large amounts of video data easier. This research provides a video feature extraction approach using a support vector machine (SVM) video classification algorithm and a mix of video and audio dual-mode characteristics. It automates the classification of cartoons, ads, music, news, and sports videos, as well as the detection of terrorist and violent moments in films. To begin, a new feature expression scheme, the MPEG-7 visual descriptor subcombination, is proposed based on an analysis of the existing video classification algorithms, with the goal of addressing the problems in these algorithms. This is accomplished by analyzing the visual differences of the five video classification algorithms. The model was able to extract 9 descriptors from the four characteristics of color, texture, shape, and motion, resulting in a new overall visual feature with good results. The results suggest that the algorithm optimizes video segmentation by highlighting disparities in feature selection between different categories of films. Second, the support vector machine’s multivideo classification performance is improved by the enhanced secondary prediction method. Finally, a comparison experiment with current related similar algorithms was conducted. The suggested method outperformed the competition in the accuracy of video classification in five different types of videos, as well as in the recognition of terrorist and violent incidents.

scholarly journals Secure Multi-User k-Means Clustering Based on Encrypted IoT Data

2019 ◽  
Vol 12 (2) ◽  
pp. 35
Author(s):  
Yanling Li ◽  
Chuansheng Wang ◽  
Qi Wang ◽  
Jieling Dai ◽  
Yushan Zhao
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Theoretical Analysis ◽  
Knowledge Discovery ◽  
Data Mining Algorithm ◽  
Personal Privacy ◽  
Encrypted Data ◽  
Mining Algorithm ◽  
Public Keys ◽  
Semantic Security

IoT technology collects information from a lot of clients, which may relate to personal privacy. To protect the privacy, the clients would like to encrypt the raw data with their own keys before uploading. However, to make use of the information, the data mining technology with cloud computing is used for the knowledge discovery. Hence, it is an emergent issue of how to effectively performing data mining algorithm on the encrypted data. In this paper, we present a k-means clustering scheme with multi-user based on the IoT data. Although, there are many privacy-preserving k-means clustering protocols, they rarely focus on the situation of encrypting with different public keys. Besides, the existing works are inefficient and impractical. The scheme we propose in this paper not only solves the problem of evaluation on the encrypted data under different public keys but also improves the efficiency of the algorithm. It is semantic security under the semi-honest model according to our theoretical analysis. At last, we evaluate the experiment based on a real dataset, and comparing with previous works, the result shows that our scheme is more efficient and practical.

The Development of Compensation System in Serbia: A Comparison of Two Successive Cranet Research Rounds

2018 ◽  
Vol 56 (1) ◽  
pp. 79-90
Author(s):  
Nemanja Berber ◽  
Agneš Slavić
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Theoretical Analysis ◽  
Compensation System ◽  
Decision Making Process ◽  
Compensation Management ◽  
Compensation Systems ◽  
Different Types ◽  
Compensation Packages

Abstract The aim of this paper is to explore the development of the compensation practice in the context of human resource management (HRM) in Serbia. The objectives are to detect the extent of the usage of different elements in the compensation packages, the level of negotiation during the determination of the basic pay, the responsibility for decision making process regarding basic pay, the extent of the usage of different types of benefits, and to explore the differences between these data in the two research periods, 2008-2010 and 2014-2016. The methodology in this paper includes the theoretical analysis of the compensation systems, as well as the comparative analysis of the data on compensation in Serbia based on the Cranet research. The sample of the study consisted of 210 organisations from Serbia, 50 organisations in the 2008-2010 period and 160 organisations in the 2014-2016 period. This paper brings new insights to the development of comparative compensation management since it points to the development/changes of the compensation practice (in years) in Serbian HRM.

scholarly journals Development of a Decision Support System for Urban Planning by Using K-means ++ Algorithm

2020 ◽  
Vol 31 (3) ◽  
pp. 78
Author(s):  
Hussein Ali Salih ◽  
Ahmed Shihab Ahmed ◽  
Jalal Qais Jameel
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Urban Planning ◽  
Decision Support System ◽  
Support System ◽  
Decision Making Process ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Coordination Framework

This article depicts a decision support system (DSS) devoted to the coordinated administration of urban frameworks. This framework defines the information and related treatments normal to a few civil managers and characterizes the necessities and functionalities of the PC devices created to enhance the conveyance, execution, and coordination of metropolitan administrations to the populace. The cooperative framework called Decision Support System for Urban Planning (DSS-UP) is made out of a universal planning and coordination framework. So, it helps the decision-making process, a DSS was created as a learning-based framework gave derivation components that empower urban architect to settle on key decisions as far as specialized meditations on civil foundations. The learning-based framework stores experts_ information and additionally answers for past issues. Preparatory execution comes about demonstrate that DSS-UP viably and effectively underpins the decision-making process identified with overseeing urban foundations by using K-means++ data mining algorithm.

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