scholarly journals Rule Based Networks: An Efficient and Interpretable Representation of Computational Models

2017 ◽  
Vol 7 (2) ◽  
pp. 111-123 ◽  
Author(s):  
Han Liu ◽  
Alexander Gegov ◽  
Mihaela Cocea
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Computational Models ◽  
Complexity Analysis ◽  
Predictive Modelling ◽  
Learning Methods ◽  
Rule Based ◽  
Rule Based System ◽  
Rule Representation

Abstract Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

scholarly journals Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

2020 ◽  
Vol 9 (5) ◽  
pp. 288-297
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
High Performance ◽  
Predictive Modelling ◽  
Medical Application ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Screening Process ◽  
Huge Data ◽  
System Data

Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

scholarly journals Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

2018 ◽  
Vol 117 (3) ◽  
pp. 387-423
Author(s):  
Marco Götz ◽  
Ferenc Leichsenring ◽  
Thomas Kropp ◽  
Peter Müller ◽  
Tobias Falk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

Combining Machine Learning and Fuzzy Rule-Based System in Automating Signal Timing Experts’ Decisions during Non-Recurrent Congestion

2020 ◽  
Vol 2674 (6) ◽  
pp. 163-176
Author(s):  
Mosammat Tahnin Tariq ◽  
Aidin Massahi ◽  
Rajib Saha ◽  
Mohammed Hadi
Keyword(s):  
Machine Learning ◽  
Recursive Partitioning ◽  
Fuzzy Rule ◽  
Traffic Signal ◽  
Fuzzy Rules ◽  
Signal Timing ◽  
Rule Based ◽  
Rule Based System ◽  
Machine Learning Model ◽  
History Of

Events such as surges in demand or lane blockages can create queue spillbacks even during off-peak periods, resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change signal timings in real time based on traffic signal engineers’ observations of incident and traffic conditions at the intersections upstream and downstream of the congested locations. Decisions to change the signal timing are governed by many factors, such as queue length, conditions of the main and side streets, potential of traffic spilling back to upstream intersections, the importance of upstream cross streets, and the potential of the queue backing up to a freeway ramp. This paper investigates and assesses automating the process of updating the signal timing plans during non-recurrent conditions by capturing the history of the responses of the traffic signal engineers to non-recurrent conditions and utilizing this experience to train a machine learning model. A combination of recursive partitioning and regression decision tree (RPART) and fuzzy rule-based system (FRBS) is utilized in this study to deal with the vagueness and uncertainty of human decisions. Comparing the decisions made based on the resulting fuzzy rules from applying the methodology with previously recorded expert decisions for a project case study indicates accurate recommendations for shifts in the green phases of traffic signals. The simulation results indicate that changing the green times based on the output of the fuzzy rules decreased delays caused by lane blockages or demand surge.

A Comparison of Rule-Based and Machine Learning Methods for Classification of Spikes in EEG

2017 ◽  
Author(s):  
Wolfgang Ganglberger ◽  
◽  
Gerhard Gritsch ◽  
Manfred M. Hartmann ◽  
Franz Fürbass ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Rule Based ◽  
Machine Learning Methods
Sign in / Sign up

Export Citation Format

Share Document