Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods

Conference paper

Adegoke, V, Chen, D, Barikzai, S and Banissi, E (2017). Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods. 2017 European Modelling Symposium (EMS). Manchester 20 - 21 Nov 2017
AuthorsAdegoke, V, Chen, D, Barikzai, S and Banissi, E
TypeConference paper
Abstract

This paper presents the empirical comparison of boosting implementation by reweighting and resampling methods. The goal of this paper is to determine which of the two methods performs better. In the study, we used four algorithms namely: Decision Stump, Neural Network, Random Forest and Support Vector Machine as base classifiers and AdaBoost as a technique to develop various ensemble models. We applied 10-fold cross validation method in measuring and evaluating the performance metrics of the models. The results show that in both methods the average of the correctly classified and incorrectly classified are relatively the same. However, average values of the RMSE in both methods are insignificantly different. The results further show that the two methods are independent of the datasets and the base classier used. Additionally, we found that the complexity of the chosen ensemble technique and boosting method does not necessarily lead to better performance.

KeywordsAdaBoost; ensemble based system; machine learning; resampling; reweighting
Year2017
Accepted author manuscript
License
File Access Level
Open
Publication dates
Print20 Nov 2017
Publication process dates
Deposited29 Nov 2017
Accepted20 Oct 2017
Permalink -

https://openresearch.lsbu.ac.uk/item/86ww8

Download files

Accepted author manuscript
Predictive Ensemble Modelling - Experimental Comparison of Boosting Implementation Methods.pdf
License: CC BY 4.0
File access level: Open

  • 151
    total views
  • 297
    total downloads
  • 1
    views this month
  • 1
    downloads this month

Export as

Related outputs

Photothermal Radiometry Data Analysis by Using Machine Learning
Xiao, P. and Chen, D. (2024). Photothermal Radiometry Data Analysis by Using Machine Learning. Sensors. 24 (10), p. 3015. https://doi.org/10.3390/s24103015
Novel Parameter-Free and Parametric Same Degree Distribution-based Dimensionality Reduction Algorithms for Trustworthy Data Structure Preserving
Hajderanj, L., Chen, D. and Dudley-Mcevoy, S. (2023). Novel Parameter-Free and Parametric Same Degree Distribution-based Dimensionality Reduction Algorithms for Trustworthy Data Structure Preserving. Information Sciences. 661, p. 120030. https://doi.org/10.1016/j.ins.2023.120030
Skin Capacitive Image Stitching and Occlusion Measurements
Ciortea, L. I., Chen, D. and Xiao, P. (2023). Skin Capacitive Image Stitching and Occlusion Measurements. Cosmetics. 10 (1), p. 32. https://doi.org/10.3390/cosmetics10010032
Understanding Cancer Patients with Diagnostically Influential Factors using High Dimensional Data Embedding
Syed, A. S., Hajderanj, L., Guo, K. and Chen, D. (2022). Understanding Cancer Patients with Diagnostically Influential Factors using High Dimensional Data Embedding. in: Imoize, A. L., Hemanth, D. J., Do, D.-T. and Sur, S. N. (ed.) Explainable Artificial Intelligence in Medical Decision Support Systems The Institution of Engineering and Technology (IET).
UAV target tracking method based on deep reinforcement learning
Zhang, H., He, P., zhang, M., Chen, D., Neretin, E. and Li, B. (2022). UAV target tracking method based on deep reinforcement learning. 2022 International Conference on Cyber-physical Social Intelligence. Nanjing, China 21 - 24 Oct 2022
Developing Phoneme-based Lip-reading Sentences System for Silent Speech Recognition
El Bialy, R., Chen, D., Fenghour, S., Hussein, W., Xiao, P., Karam, O. H. and Li, B. (2022). Developing Phoneme-based Lip-reading Sentences System for Silent Speech Recognition. CAAI Transactions on Intelligence Technology. 8 (1), pp. 128-139. https://doi.org/10.1049/cit2.12131
An effective context-focused hierarchical mechanism for task-oriented dialogue response generation
Zhao, M., Wang, L., Jiang, Z., Li, R., Lu, X., Hu, Z. and Chen, D. (2022). An effective context-focused hierarchical mechanism for task-oriented dialogue response generation. Computational Intelligence. 38 (5), pp. 1831-1858. https://doi.org/10.1111/coin.12544
The Effect of Sun Tan Lotion on Skin By Using Skin TEWL and Skin Water Content Measurements
Xiao, P. and Chen, D. (2022). The Effect of Sun Tan Lotion on Skin By Using Skin TEWL and Skin Water Content Measurements. MDPI Sensors. 22. https://doi.org/10.3390/s22093595
A Qualitative Research Study of the Tech Startup Journey through Entrepreneurial Pivoting
Sala, P., Philbin, S. and Barikzai, S. (2022). A Qualitative Research Study of the Tech Startup Journey through Entrepreneurial Pivoting. International Journal of Entrepreneurial Behavior & Research. https://doi.org/10.1108/IJEBR-07-2021-0528
Few-shot Object Recognition based on Three-Way Decision and Active Learning
Li, B., Luo, S., Wang, J., Tian, L. and Chen, D. (2022). Few-shot Object Recognition based on Three-Way Decision and Active Learning. Visual Computer . 37.
An Effective Conversion of Visemes to Words for High-Performance Automatic Lipreading.
Fenghour, S., Chen, D., Guo, K., Li, B. and Xiao, P. (2021). An Effective Conversion of Visemes to Words for High-Performance Automatic Lipreading. Sensors. 21 (23). https://doi.org/s21237890
UAV visual flight control method based on deep reinforcement learning
Bai, S., Li, B., Gan, Z. and Chen, D. (2021). UAV visual flight control method based on deep reinforcement learning. 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI). https://doi.org/10.1109/iccsi53130.2021.9736242
UAV flight control method based on deep reinforcement learning
Bai, S., Li, B., Gan, Z. and Chen, D. (2021). UAV flight control method based on deep reinforcement learning. 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI). Beijing, China 18 Dec 2021 - 20 Mar 2022 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCSI53130.2021.9736242
ME‐MADDPG: An efficient learning‐based motion planning method for multiple agents in complex environments
Wan, K., Wu, D., Li, B., Gao, X., Hu, Z. and Chen, D. (2021). ME‐MADDPG: An efficient learning‐based motion planning method for multiple agents in complex environments. International Journal of Intelligent Systems. 37 (3), pp. 2393-2427. https://doi.org/10.1002/int.22778
Learning the structure of Bayesian networks with ancestral and/or heuristic partition
Tan, X., Gao, X., Wang, Z., Han, H., Liu, X. and Chen, D. (2021). Learning the structure of Bayesian networks with ancestral and/or heuristic partition. Information Sciences. https://doi.org/10.1016/j.ins.2021.10.052
Deep Learning-based Automated Lip-Reading: A Survey
Fenghour, S., Chen, D., Guo, K., Li, B. and Xiao, P. (2021). Deep Learning-based Automated Lip-Reading: A Survey. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3107946
Deep Learning Causal Attributions of Breast Cancer
Chen, D., Hajderanj, L., Mallet, S., Camenen, P., Li, B., Hao, R. and Zhao, E. (2021). Deep Learning Causal Attributions of Breast Cancer. in: Arai, K. (ed.) Intelligent Computing, Proceedings of the 2021 Computing Conference, Lecture Notes in Networks and Systems, Vol 285, Intelligent Computing (A. Kohei, Editor) Springer.
Exploring entrepreneurial pivoting and the factors that trigger pivots by tech startups
Sala, P., Philbin, S. and Barikzai, S. (2021). Exploring entrepreneurial pivoting and the factors that trigger pivots by tech startups. TEMSCON’21: 2021 IEEE Technology & Engineering Management Conference – Europe.. Virtual Conference 17 - 21 May 2021 Institute of Electrical and Electronics Engineers (IEEE).
The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study
Hajderanj, L., Chen, D. and Weheliye, I. (2021). The Impact of Supervised Manifold Learning on Structure Preserving and Classification Error: A Theoretical Study. IEEE Access. 9. https://doi.org/10.1109/ACCESS.2021.3066259
Enhancing Transformer-based language models with Commonsense Representations for Knowledge-driven Machine Comprehension
Li, R., Jiang, Z., Wang, L., Lu, X., Zhao, M. and Chen, D. (2021). Enhancing Transformer-based language models with Commonsense Representations for Knowledge-driven Machine Comprehension. Knowledge-Based Systems. 220, p. 106936. https://doi.org/10.1016/j.knosys.2021.106936
The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms
Xiao, P., Zhang, Xu, Pan, Wei, Ou, Xiang, Bontozoglou, C., Chirikhina, E. and Chen, D. (2020). The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms. Cosmetics. 7 (3), p. e67. https://doi.org/10.3390/cosmetics7030067
Measuring consumer behavioural intention to accept technology: Towards autonomous vehicles technology acceptance model (AVTAM)
Ubakanma, G., Seuwou, P., Chrysoulas, C. and Banissi, E. (2020). Measuring consumer behavioural intention to accept technology: Towards autonomous vehicles technology acceptance model (AVTAM). WorldCIST: World Conference on Information Systems and Technologies. Budva, Montenegro 07 - 10 Apr 2020 Springer. https://doi.org/10.1007/978-3-030-45688-7
Three‐way decision of target threat decision making based on adaptive threshold algorithms
Li, B., Tian, Li., Han, Y. and Chen, D. (2020). Three‐way decision of target threat decision making based on adaptive threshold algorithms. The Journal of Engineering. 2020 (13), pp. 293-297. https://doi.org/10.1049/joe.2019.1202
Effectiveness analysis of ship formation air defence based on deep belief network
Li, B., Luo, H., Wang, Y. and Chen, D. (2020). Effectiveness analysis of ship formation air defence based on deep belief network. The Journal of Engineering. 2020 (13), pp. 394-398. https://doi.org/10.1049/joe.2019.1201
Maneuvering target tracking of UAV based on MN-DDPG and transfer learning
Li, B., Yang, Z.P., Chen, D.Q., Liang, S.Y. and Ma, H. (2020). Maneuvering target tracking of UAV based on MN-DDPG and transfer learning. Defence Technology. https://doi.org/10.1016/j.dt.2020.11.014
Investigating the Entrepreneurial Pivoting Experience of UK-based Technology Start-ups
Sala, P., Philbin, S. and Barikzai, S. (2020). Investigating the Entrepreneurial Pivoting Experience of UK-based Technology Start-ups. ISPIM (International Society for Professional Innovation Management) Connects Global 2020: Celebrating the World of Innovation - Virtual, 6-8 December 2020. Virtual Conference 06 - 08 Dec 2020
Lip Reading Sentences Using Deep Learning with Only Visual Cues
Fenghour, S., Chen, D., Guo, K. and Xiao, P. (2020). Lip Reading Sentences Using Deep Learning with Only Visual Cues. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3040906
Deep Learning Causal Attributions of Breast Cancer
Chen, D, Hajderanj, L, Mallet, S, Camenen, P, Li, B, Ren, H and Zhao, E (2020). Deep Learning Causal Attributions of Breast Cancer. Computing 2021. London 15 - 16 Jul 2021 The Science and Information (SAI) Organization. https://doi.org/10.1007/978-3-030-80129-8_10
UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning
Li, B., Gan, Z., Chen, D. and Aleksandrovich, D.S. (2020). UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning. Remote Sensing. 12 (22), p. 3789. https://doi.org/10.3390/rs12223789
Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing
Hajderanj, L., Chen, D., Grisan, E. and Dudley-McEvoy, S (2020). Single- and Multi-Distribution Dimensionality Reduction Approaches for a Better Data Structure Capturing. IEEE Access. 8, pp. 207141 - 207155. https://doi.org/10.1109/ACCESS.2020.3038460
Learning Bayesian Networks based on Order Graph with Ancestral Constraints
Wang, Z., Gao, X., Tian, X., Yang, Y. and Chen, D. (2020). Learning Bayesian Networks based on Order Graph with Ancestral Constraints. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2020.106515
An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration
Li, B., Liang, S., Tian, L., Chen, D. and Zhang, M. (2020). An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration. Mathematical Problems in Engineering. p. 5391479. https://doi.org/10.1155/2020/5391479
An adaptive dwell time scheduling model for phased array radar based on three-way decision
Li, B., Tian, L., Chen, D. and Liang, S. (2020). An adaptive dwell time scheduling model for phased array radar based on three-way decision. Journal of Systems Engineering and Electronics. pp. 500-509. https://doi.org/10.23919/JSEE.2020.000030
Conceptualising Green Awareness as Moderator in Technology Acceptance Model for Green IS/IT
Ashiq, S., Banissi, E. and Chrysoulas, C. (2019). Conceptualising Green Awareness as Moderator in Technology Acceptance Model for Green IS/IT. 2019 International Conference on Innovative Computing (ICIC). 01 - 02 Nov 2019 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIC48496.2019.8966710
Intelligent Aircraft Maneuvering Decision Based on CNN
Li, B., Liang, S., Tian, L. and Chen, D. (2019). Intelligent Aircraft Maneuvering Decision Based on CNN. Proceedings of the 3rd International Conference on Computer Science and Application Engineering. (138). https://doi.org/10.1145/3331453.3362046
Intelligent Attitude Control of Aircraft Based on LSTM
Li, B, Gao, P, Li, X and Chen, D (2019). Intelligent Attitude Control of Aircraft Based on LSTM. 3rd International Conference on Artificial Intelligence Applications and Technologies. Beijing, China 01 - 03 Aug 2019 IOP Publishing. https://doi.org/10.1088/1757-899X/646/1/012013
A Task Scheduling Algorithm for Phased Array Radar Based on Dynamic Three-way Decision
Li, B., Tian, L., Chen, D. and Han, Y. (2019). A Task Scheduling Algorithm for Phased Array Radar Based on Dynamic Three-way Decision. Sensors. 20 (1). https://doi.org/10.3390/s20010153
Intelligent Flight Control of Combat Aircraft Based on Autoencoder
Li, B., Gao, P., Liang, S. and Chen, D. (2019). Intelligent Flight Control of Combat Aircraft Based on Autoencoder. 2019 The 4th International Conference on Robotics, Control and Automation. GuangZhou 26 - 28 Jul 2019 https://doi.org/10.1145/3351180.3351210
Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet
Zhang, X., Pan, W., Bontozoglou, C., Chirikhina, E., Chen, D. and Xiao, P. (2019). Skin Capacitive Imaging Analysis Using Deep Learning GoogLeNet. Computing Conference 2020. London, UK 16 - 17 Jul 2019 Springer.
FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction
Wang, L.F., Lu, X., Jiang, Z., Zhang, Z., Li, R., Zhao, M. and Chen, D. (2019). FRS: A Simple Knowledge Graph Embedding Model for Entity Prediction. Mathematical Biosciences and Engineering. 16 (6), pp. 7789-7807. https://doi.org/10.3934/mbe.2019391
The Future of Mobility with Connected and Autonomous Vehicles in Smart Cities
Seuwou, P., Banissi, E. and Ubakanma, G. (2019). The Future of Mobility with Connected and Autonomous Vehicles in Smart Cities. in: Farsi, M., Daneshkhah, A., Hosseinian-Far, A. and Jahankhani, H. (ed.) Digital Twin Technologies and Smart Cities Springer. pp. 37-52
Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study
Chen, D., Guo, K. and Li, B. (2019). Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study. 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019). Havana, Cuba 28 - 31 Oct 2019 https://doi.org/10.1007/978-3-030-33904-3_16
Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method
Yang, Y, Gao, X, Guo, Z and Chen, D (2019). Learning Bayesian Networks using the Constrained Maximum a Posteriori Probability Method. Pattern Recognition. 91, pp. 123-134. https://doi.org/10.1016/j.patcog.2019.02.006
Learning Bayesian network parameters via minimax algorithm
Gao, X, Gao, G, Ren, H, Chen, D and He, C (2019). Learning Bayesian network parameters via minimax algorithm. International Journal of Approximate Reasoning. 108, pp. 62-75. https://doi.org/10.1016/j.ijar.2019.03.001
Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models
Adegoke, V, Chen, D and Banissi, E (2019). Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models. International Journal of Computer Information Systems and Industrial Management. 11, pp. 82-100.
A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification
Hajderanj, L, Weheliye, I and Chen, D (2019). A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification. 2019 8th International Conference on Software and Information Engineering. Cairo 09 - 12 Apr 2019
Recurrent Neural Networks for Decoding Lip Read Speech
Fenghour, S, Chen, D and Xiao, P (2019). Recurrent Neural Networks for Decoding Lip Read Speech. 2019 8th International Conference on Software and Information Engineering (ICSIE 2019). Cairo 09 - 12 Apr 2019
Decoder-Encoder LSTM for Lip Reading
Fenghour, S., Chen, D. and Xiao, P. (2019). Decoder-Encoder LSTM for Lip Reading. Proceedings of the 2019 8th International Conference on Software and Information Engineering. https://doi.org/10.1145/3328833.3328845
Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition
Adegoke, V, Chen, D, Banissi, E and Barikzai, S (2019). Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition. The 10th International Conference on Soft Computing and Pattern Recognition. Porto, Portugal 13 - 15 Dec 2018
Distributed deep networks based on Bagging-Down SGD algorithm
Qin, C, Gao, X and Chen, D (2019). Distributed deep networks based on Bagging-Down SGD algorithm. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics. 41 (5), pp. 1021-1027. https://doi.org/10.3969/j.issn.1001-506X.2019.05.13
Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach
Chen, D, Hajderanj, L and Fiske, J (2019). Towards automated cost analysis, benchmarking and estimating in construction: a machine learning approach. 13th Multi Conference on Computer Science and Information Systems (MCCSIS). Porto, Portugal 16 - 18 Jul 2019
Design of a voice control 6DoF grasping robotic arm based on ultrasonic sensor, computer vision and Alexa voice assistance
Wang, Z, Chen, D and Xiao, P (2019). Design of a voice control 6DoF grasping robotic arm based on ultrasonic sensor, computer vision and Alexa voice assistance. International Conference on Information Technology in Medicine and Education. Qingdao, China 23 - 25 Aug 2019 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ITME.2019.00150
Preface
Banissi, E, Sarfraz, M and Fakir, M (2016). Preface. in: 2016 13th International Conference Computer Graphics, Imaging and Visualization Institute of Electrical and Electronics Engineers (IEEE). pp. xii
Preface
Banissi, E and Wyeld, T (2016). Preface. in: 2016 20th International Conference Information Visualisation (IV) Institute of Electrical and Electronics Engineers (IEEE).
Preface
Banissi, E (2017). Preface. in: 2017 21st International Conference Information Visualisation (IV) Institute of Electrical and Electronics Engineers (IEEE). pp. xii
Bertin’s forgotten typographic variables and new typographic visualization
Brath, R. and Banissi, E. (2018). Bertin’s forgotten typographic variables and new typographic visualization. Cartography and Geographic Information Science. 46 (2), pp. 119-139. https://doi.org/10.1080/15230406.2018.1516572
Using Type to Add Data to Data Visualizations
Brath, R and Banissi, E (2015). Using Type to Add Data to Data Visualizations. TypeCon 2015. Denver, Colorado, USA 12 - 16 Aug 2015 Society of Typographic Aficionados.
Guest editorial: Special issue on information visualisation
Banissi, E and Huang, W (2018). Guest editorial: Special issue on information visualisation. Journal of Visual Languages and Computing. 44, pp. 70-71. https://doi.org/10.1016/j.jvlc.2017.11.005
Stem and leaf plots extended for text visualizations
Brath, R and Banissi, E (2018). Stem and leaf plots extended for text visualizations. 14th International Conference on Computer Graphics, Imaging and Visualization. Marrakesh, Morocco 23 - 25 May 2017 Institute of Electrical and Electronics Engineers (IEEE). pp. 99-104 https://doi.org/10.1109/CGiV.2017.32
Preface
Banissi, E, Sarfraz, M, Zeroual, A and Fakir, M (2018). Preface. 14th International Conference on Computer Graphics, Imaging and Visualization. Marrakesh, Morocco 23 - 25 May 2017 The Institute of Electrical and Electronics Engineers, Inc.. pp. viii https://doi.org/10.1109/CGiV.2017.4
Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards
Chen, D, Sanz, BM and Zhao, E (2018). Visual analytics in the public sector: An analysis on diversities and similarities of London’s wards. International Conference on Big Data Analytics, Data Mining and Computational Intelligence 2018 (BigDaCI 2018). Madrid, Spain 18 - 20 Jul 2018 Bigdaci.
Contour Mapping for Speaker-Independent Lip Reading System
Fenghour, S, Chen, D and Xiao, P (2018). Contour Mapping for Speaker-Independent Lip Reading System. The 11th International Conference on Machine Vision (ICMV 2018). Munich, Germany 01 - 03 Nov 2018
Hardware aspects of algorithm generation
Pitteway, MLV and Banissi, E (1987). Hardware aspects of algorithm generation. in: Theoretical foundations of computer graphics and CAD Springer.
Computer Graphics, Imaging & Visualization — New Techniques and Trends
Banissi, E, Sarfraz, M and Fakir, M Banissi, E (ed.) (2016). Computer Graphics, Imaging & Visualization — New Techniques and Trends. CPS.
User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security
Ubakanma, G, Seuwou, P and Banissi, E (2017). User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. in: Global Security, Safety and Sustainability - The Security Challenges of the Connected World Springer. pp. 230-251
QL-282 - Emerging Medical Sensory Technology
Banissi, E and Benjamin, EL (2016). QL-282 - Emerging Medical Sensory Technology. Qinetiq.
Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles
Seuwou, P., Banissi, E., Ubakanma, G., Sherif, S., M. and Healey, A. (2017). Actor-Network Theory as a Framework to Analyse Technology Acceptance Model’s External Variables: The Case of Autonomous Vehicles. in: Global Security, Safety and Sustainability: The Security Challenges of the Connected World switzerland pp. 305-320
Multivariate label-based thematic maps
Brath, R and Banissi, E (2017). Multivariate label-based thematic maps. International Journal of Cartography. 3 (1), pp. 45-60. https://doi.org/10.1080/23729333.2017.1301346
User concerns about Facebook: Are they important
Al-Shamaileh, O, Aloudat, A and Barikzai, S (2017). User concerns about Facebook: Are they important. ICIT 2017 The 8th International Conference on Information Technology. Amman, Jordan 17 - 18 May 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.15849/icit.2017.0000
Stem & Leaf Plots Extended to Various Ranges of Text
Brath, R and Banissi, E (2017). Stem & Leaf Plots Extended to Various Ranges of Text. 14th International Conference Computer Graphics, Imaging and Visualization. Marrakesh, Morocco 22 - 25 May 2017
Microtext Line Charts
Brath, R and Banissi, E (2017). Microtext Line Charts. 21st International Conference Information Visualisation. London 11 - 14 Jul 2017
Learning Bayesian Network Parameters from a Small Data Set: A Further Constrained Qualitatively Maximum a Posteriori Method
Guo, Zhi-gao, Gao, Xiao-guang, Hao, Ren, Yang, Yu, Di, Ruo-hai and Chen, D (2017). Learning Bayesian Network Parameters from a Small Data Set: A Further Constrained Qualitatively Maximum a Posteriori Method. International Journal of Approximate Reasoning. 91 (Dec), pp. 22-35. https://doi.org/10.1016/j.ijar.2017.08.009
Feature Extraction and Labelling Large Data Sets Using Deep Learning
Chen, D (2017). Feature Extraction and Labelling Large Data Sets Using Deep Learning. RESEARCHER LINK: Smart Technology for Fighting Virus Epidemics & Bioinformatics. Recife, Pernambuco, Brazil 10 - 13 Sep 2017
Prediction of Breast Cancer Survivability using Ensemble Algorithms
Adegoke, V, Chen, D, Banissi, E and Barikzai, S (2017). Prediction of Breast Cancer Survivability using Ensemble Algorithms. International Conference on Smart System and Technologies 2017 (SST 2017),. Osijek, Croatia 18 - 20 Oct 2017
Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels
Brath, R and Banissi, E (2016). Font attributes enrich knowledge maps and information retrieval: Skim formatting, proportional encoding, text stem and leaf plots, and multi-attribute labels. International Journal on Digital Libraries. 18 (1), pp. 5-24. https://doi.org/10.1007/s00799-016-0168-4
Making Better Use of Big Data
Chen, D (2016). Making Better Use of Big Data. LSBU Enterprise Count Event, March 2016. London Southbank University 18 - 18 Mar 2016 London South Bank University.
Big Data Analytics In The Public Sector: A Case Study Of NEET Analysis For The London Boroughs
Chen, D, Asaolu, B and Qin, C (2016). Big Data Analytics In The Public Sector: A Case Study Of NEET Analysis For The London Boroughs. International Conference on Big Data Analytics, Data Mining and Computational Intelligence. Funchal, Madeira, Portugal 02 - 04 Jul 2016
Typographic sets: Labeled set elements with font attributes
Brath, R and Banissi, E (2016). Typographic sets: Labeled set elements with font attributes. International Workshop on Set Visualization and Reasoning. Philadelphia, United States of America 07 Aug 2016 pp. 29-43
Set Visualization
Brath, R and Banissi, E (2016). Set Visualization. SetVR 2016. Philadelphia, USA 07 - 10 Aug 2016
Using Typography to Expand the Design Space of Data Visualization
Brath, R and Banissi, E (2016). Using Typography to Expand the Design Space of Data Visualization. She Ji: The Journal of Design, Economics, and Innovation. 2 (1), pp. 59-87. https://doi.org/10.1016/j.sheji.2016.05.003
On Distributed Deep Network for Processing Large-Scale Sets of Complex Data
Qin, C, Gao, X and Chen, D (2016). On Distributed Deep Network for Processing Large-Scale Sets of Complex Data. 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). Hangzhou, China. 27 - 28 Aug 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IHMSC.2016.55
Nurturing Women Entrepreneurship, UK and Bahrain perspectives
Taqi, N, Barikzai, S and Cole, L (2016). Nurturing Women Entrepreneurship, UK and Bahrain perspectives. International Entrepreneurship Educators Conference. Liverpool John Moores University, Liverpool, UK 07 - 09 Sep 2016
Mutlivariate Labeled Cartograms
Brath, R and Banissi, E (2016). Mutlivariate Labeled Cartograms. North American Cartographic Information Society Annual Meeting. Colorado Springs, USA 19 - 22 Oct 2016
Evaluation of Visualization by Critiques
Brath, R and Banissi, E (2016). Evaluation of Visualization by Critiques. BELIV '16 Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization. Baltimore, MD, USA 24 Oct 2016 ACM. https://doi.org/10.1145/2993901.2993904
A Bayesian Approach to Learn Bayesian Networks Using Data and Constraints
Gao, X, Yu, Y, Zhi-gao, G and Chen, D (2016). A Bayesian Approach to Learn Bayesian Networks Using Data and Constraints. 23rd International Conference on Pattern Recognition (ICPR 2016). Cancún, México 04 - 08 Dec 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICPR.2016.7900204
Evaluating Lossiness and Fidelity in Visualization
Banissi, E and Brath, R (2015). Evaluating Lossiness and Fidelity in Visualization. Visualization and Data Analysis 2015. San Francisco, USA 08 - 12 Feb 2015 SPIE.
Using text in visualizations for micro/macro readings
Brath, R and Banissi, E (2015). Using text in visualizations for micro/macro readings. IUI Workshop on Visual Text Analytics. Atlanta, USA 29 Mar 2015 ACM. https://doi.org/10.13140/RG.2.1.1651.2084
Big Data Analytics System for Fact/Data-driven Decision Making
Chen, D (2015). Big Data Analytics System for Fact/Data-driven Decision Making. The Royal Statistical Society, Business and Industry Section. London, UK 18 Nov 2015 Royal Statistical Society .
Using font attributes in knowledge maps and information retrieval
Brath, R and Banissi, E (2014). Using font attributes in knowledge maps and information retrieval. Knowledge Maps and Information Retrieval 2014. London 11 Sep 2014 CEUR Workshop Proceedings.
The Design Space of Typeface
Brath, R and Banissi, E (2014). The Design Space of Typeface. IEEE Conference on Information Visualisation VIS 2014. Paris, France 09 - 14 Nov 2015 Institute of Electrical and Electronics Engineers (IEEE).
Determining Key (Predictor) Modules for Early Identification of Students At-Risk
Chen, D and Elliott, G (2013). Determining Key (Predictor) Modules for Early Identification of Students At-Risk. International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013). Beijing, China 19 - 20 Dec 2013 Atlantis Press. https://doi.org/10.2991/icaiees-13.2013.22
Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining
Chen, D (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management. 19 (3), pp. 197-208. https://doi.org/10.1057/dbm.2012.17