Alexander Gegov

Rule Based Systems for Big Data: A Machine Learning Approach

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

Multi-layer Decision methodology For ranking Z-numbers

AbstractThe new concept of a Z – number has been recently introduced in decision making analysis. This concept is capable of effectively dealing with uncertainty in information about a decision. As this concept is relatively new in fuzzy sets, its underlying theoretical aspects have not been established yet. In this paper, a multi-layer methodology for ranking Z – numbers is proposed for the first time. This methodology consists of two layers: Z – number conversion as the first layer and fuzzy number ranking as the second layer. In this study, the conversion methodology of Z – numbers into fuzzy numbers is extended to conversion into standardised generalised fuzzy number so that the methodology is applicable to both positive and negative data values. The methodology is validated by means of thorough comparison with some established ranking methods for consistency purposes. This methodology is considered as a generic decision making procedure, especially when Z – numbers are applied to real decision making...

Interactive TOPSIS Based Group Decision Making Methodology Using Z-Numbers

AbstractThe ability in providing result that is consistent with actual ranking remains the major concern in group decision making environment. The main aim of this paper is to introduce a novel modification of TOPSIS method to facilitate multi criteria decision making problems based on the concept of Z-numbers called Z-TOPSIS. The proposed method is adequate and intuitive in giving meaningful structure for formalizing information of a decision making problem, as it takes into account the decision makers’ reliability. This study also provides bridge with some established knowledge in fuzzy sets to certain extend as to strengthen the concept of ranking alternatives using Z – numbers. To ensure practicality and effectiveness of proposed method, stock selection problem is studied. The ranking based on proposed method is validated comparatively using spearman rho rank correlation. Based on the analysis, the proposed method outperforms the established TOPSIS methods in term of ranking performance.

AI tools for use in assembly automation and some examples of recent applications

Purpose – This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.Design/methodology/approach – Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.Findings – Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.Research limitations/implications – Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single ...

Improving ability of tele‐operators to complete progressively more difficult mobile robot paths using simple expert systems and ultrasonic sensors

– The purpose of this paper is to describe the use of simple expert systems to improve the performance of tele‐operated mobile robots and ultrasonic sensor systems. The expert systems interpret data from the joystick and sensors and identify potentially hazardous situations and then recommend safe courses of action so that tele‐operated mobile‐robot tasks can be completed more quickly., – The speed of a tele‐operator in completing progressively more complicated driving tasks is investigated while using a simple expert system. Tele‐operators were timed completing a series of tasks using a joystick to control a mobile robot through a simple expert system that assisted them with driving the robot while using ultrasonic sensors to avoid obstacles. They either watched the robot while operating it or sat at a computer and viewed scenes remotely on a screen from a camera mounted on the robot. Tele‐operators completed tests with the simple expert system and the sensors connected. The system used an umbilical cable to connect to the robot., – The simple expert systems consistently performed faster than the other systems. Results are compared with the most recently published results and show a significant improvement. In addition, in simple environments, tele‐operators performed better without a sensor system to assist them but in more complicated environments than tele‐operators performed better with the sensor systems to assist., – Simple expert systems are shown to improve the operation of a tele‐operated mobile robot with an obstacle avoidance systems fitted., – Tele‐operated systems rely heavily on visual feedback and experienced operators. This paper investigates how to make tasks easier. Simple expert systems are shown to improve the operation of a tele‐operated mobile robot. The paper also suggests that the amount of sensor support should be varied depending on circumstances., – The simple expert systems are shown in this paper to improve the operation of a tele‐operated mobile robot. Tele‐operators completed tests with the simple expert system and the sensors connected. The results are compared with a tele‐operator driving a mobile robot without any assistance from the expert systems or sensors and they show a significant improvement.

Fuzzy Networks for Complex Systems

This book introduces the novel concept of a fuzzy network whose nodes are rule bases and the connections between the nodes are the interactions between the rule bases in the form of outputs fed as inputs. The concept is presented as a systematic study for improving the feasibility and transparency of fuzzy models by means of modular rule bases whereby the model accuracy and efficiency can be optimised in a flexible way. The study uses an effective approach for fuzzy rule based modelling of complex systems that are characterised by attributes such as nonlinearity, uncertainty, dimensionality and structure.The approach is illustrated by formal models for fuzzy networks, basic and advanced operations on network nodes, properties of operations, feedforward and feedback fuzzy networks as well as evaluation of fuzzy networks. The results are demonstrated by numerous examples, two case studies and software programmes within the Matlab environment that implement some of the theoretical methods from the book. The book shows the novel concept of a fuzzy network with networked rule bases as a bridge between the existing concepts of a standard fuzzy system with a single rule base and a hierarchical fuzzy system with multiple rule bases.

Rule based networks: an efficient and interpretable representation of computational models

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.

Interpretability of computational models for sentiment analysis

Sentiment analysis, which is also known as opinion mining, has been an increasingly popular research area focusing on sentiment classification/regression. In many studies, computational models have been considered as effective and efficient tools for sentiment analysis . Computational models could be built by using expert knowledge or learning from data. From this viewpoint, the design of computational models could be categorized into expert based design and data based design. Due to the vast and rapid increase in data, the latter approach of design has become increasingly more popular for building computational models. A data based design typically follows machine learning approaches, each of which involves a particular strategy of learning. Therefore, the resulting computational models are usually represented in different forms. For example, neural network learning results in models in the form of multi-layer perceptron network whereas decision tree learning results in a rule set in the form of decision tree. On the basis of above description, interpretability has become a main problem that arises with computational models. This chapter explores the significance of interpretability for computational models as well as analyzes the factors that impact on interpretability. This chapter also introduces several ways to evaluate and improve the interpretability for computational models which are used as sentiment analysis systems. In particular, rule based systems , a special type of computational models, are used as an example for illustration with respects to evaluation and improvements through the use of computational intelligence methodologies.

Unified Framework for Construction of Rule Based Classification Systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfitting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suitable structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

Induction of Modular Classification Rules by Information Entropy Based Rule Generation

Prism has been developed as a modular classification rule generator following the separate and conquer approach since 1987 due to the replicated sub-tree problem occurring in Top-Down Induction of Decision Trees (TDIDT). A series of experiments have been done to compare the performance between Prism and TDIDT which proved that Prism may generally provide a similar level of accuracy as TDIDT but with fewer rules and fewer terms per rule. In addition, Prism is generally more tolerant to noise with consistently better accuracy than TDIDT. However, the authors have identified through some experiments that Prism may also give rule sets which tend to underfit training sets in some cases. This paper introduces a new modular classification rule generator, which follows the separate and conquer approach, in order to avoid the problems which arise with Prism. In this paper, the authors review the Prism method and its advantages compared with TDIDT as well as its disadvantages that are overcome by a new method using Information Entropy Based Rule Generation (IEBRG). The authors also set up an experimental study on the performance of the new method in classification accuracy and computational efficiency. The method is also evaluated comparatively with Prism.