Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers

Ant colony optimization algorithm for rule based classification: Issues and potential

Classification rule discovery using ant colony optimization (ACO) imitates the foraging behavior of real ant colonies. It is considered as one of the successful swarm intelligence metaheuristics for data classification. ACO has gained importance because of its stochastic feature and iterative adaptation procedure based on positive feedback, both of which allow for the exploration of a large area of the search space. Nevertheless, ACO also has several drawbacks that may reduce the classification accuracy and the computational time of the algorithm. This paper presents a review of related work of ACO rule classification which emphasizes the types of ACO algorithms and issues. Potential solutions that may be considered to improve the performance of ACO algorithms in the classification domain were also presented. Furthermore, this review can be used as a source of reference to other researchers in developing new ACO algorithms for rule classification

Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

Adaptive parameter control strategy for ant-miner classification algorithm

Pruning is the popular framework for preventing the dilemma of over fitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-Ant Miner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers

An adaptive ant colony optimization algorithm for rule-based classification

Classification is an important data mining task with different applications in many fields. Various classification algorithms have been developed to produce classification models with high accuracy. Differing from other complex and difficult classification models, rules-based classification algorithms produce models which are understandable for users. Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. However, the Ant-Miner has overfitting and easily falls into local optima problems which resulted in low classification accuracy and complex classification rules. In this study, a new Ant-Miner classifier is developed, named Adaptive Genetic Iterated-AntMiner (AGI-AntMiner) that aims to avoid local optima and overfitting problems. The components of AGI-AntMiner includes: i) an Adaptive AntMiner which is a prepruning technique to dynamically select the appropriate threshold based on the quality of the rules; ii) Genetic AntMiner that improves the post-pruning by adding/removing terms in a dual manner; and, iii) an Iterated Local Search-AntMiner that improves exploitation based on multiple-neighbourhood structure. The proposed AGI-AntMiner algorithm is evaluated on 16 benchmark datasets of medical, financial, gaming and social domains obtained from the University California Irvine repository. The algorithm’s performance was compared with other variants of Ant-Miner and state-of-the-art rules-based classification algorithms based on classification accuracy and model complexity. Experimental results proved that the proposed AGI-AntMiner algorithm is superior in two (2) aspects. Hybridization of local search in AGI-AntMiner has improved the exploitation mechanism which leads to the discovery of more accurate classification rules. The new pre-pruning and postpruning techniques have improved the pruning ability to produce shorter classification rules which are easier to interpret by the users. Thus, the proposed AGI-AntMiner algorithm is capable in conducting an efficient search in finding the best classification rules that balance the classification accuracy and model complexity to overcome overfitting and local optima problems

Cognitive-support code review tools : improved efficiency of change-based code review by guiding and assisting reviewers

Code reviews, i.e., systematic manual checks of program source code by other developers, have been an integral part of the quality assurance canon in software engineering since their formalization by Michael Fagan in the 1970s. Computer-aided tools supporting the review process have been known for decades and are now widely used in software development practice. Despite this long history and widespread use, current tools hardly go beyond simple automation of routine tasks. The core objective of this thesis is to systematically develop options for improved tool support for code reviews and to evaluate them in the interplay of research and practice. The starting point of the considerations is a comprehensive analysis of the state of research and practice. Interview and survey data collected in this thesis show that review processes in practice are now largely change-based, i.e., based on checking the changes resulting from the iterative-incremental evolution of software. This is true not only for open source projects and large technology companies, as shown in previous research, but across the industry. Despite the common change-based core process, there are various differences in the details of the review processes. The thesis shows possible factors influencing these differences. Important factors seem to be the process variants supported and promoted by the used review tool. In contrast, the used tool has little influence on the fundamental decision to use regular code reviews. Instead, the interviews and survey data suggest that the decision to use code reviews depends more on cultural factors. Overall, the analysis of the state of research and practice shows that there is a potential for developing better code review tools, and this potential is associated with the opportunity to increase efficiency in software development. The present thesis argues that the most promising approach for better review support is reducing the reviewer's cognitive load when reviewing large code changes. Results of a controlled experiment support this reasoning. The thesis explores various possibilities for cognitive support, two of these in detail: Guiding the reviewer by identifying and presenting a good order of reading the code changes being reviewed, and assisting the reviewer through automatic determination of change parts that are irrelevant for review. In both cases, empirical data is used to both generate and test hypotheses. In order to demonstrate the practical suitability of the techniques, they are also used in a partner company in regular development practice. For this evaluation of the cognitive support techniques in practice, a review tool which is suitable for use in the partner company and as a platform for review research is needed. As such a tool was not available, the code review tool "CoRT" has been developed. Here, too, a combination of an analysis of the state of research, support of design decisions through scientific studies and evaluation in practical use was employed. Overall, the results of this thesis can be roughly divided into three blocks: Researchers and practitioners working on improving review tools receive an empirically and theoretically sound catalog of requirements for cognitive-support review tools. It is available explicitly in the form of essential requirements and possible forms of realization, and additionally implicitly in the form of the tool "CoRT". The second block consists of contributions to the fundamentals of review research, ranging from the comprehensive analysis of review processes in practice to the analysis of the impact of cognitive abilities (specifically, working memory capacity) on review performance. As the third block, innovative methodological approaches have been developed within this thesis, e.g., the use of process simulation for the development of heuristics for development teams and new approaches in repository and data mining

Power and trust : analysis of the effects of deglobalisation and financial technology in the United Kingdom, United States and European Union

This thesis researched the effects deglobalisation and financial technology are having on the United Kingdom, United States and European Union since the 2008 Global Financial Crisis (GFC). Particular attention is paid to financial services, as it is the industry most closely related to the GFC and is central to the concept of financial technology.It begins by examining the development and dynamics of the globalised economy, defines what deglobalisation is, reviews financial crises predating the GFC and introduces the concept and history of financial technology. Analysis then focuses on the current financial regulatory landscape of the EU, UK and US. It then reviews technological developments that have occurred in the aftermath of the GFC to determine which have the greatest likelihood for adoption by the financial services industry within the next five to ten years and how they are most likely to be implemented. Particular attention is given to blockchain and smart contracts and their potential for business integration.It then assesses financial legislation passed during Trump’s tenure to determine its ramifications. The thesis concludes with analysis of the state of deglobalisation and socioeconomic conditions, especially within the UK as of 2021, the outcome of the finalised Brexit agreement for financial services and how they have affected the UK economy. This is to determine what the consequences of the period of deglobalisation from 2016 to early 2021 have ultimately meant for the US, UK and EU