Unsupervised text Feature Selection using memetic Dichotomous Differential Evolution

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection

Document clustering with optimized unsupervised feature selection and centroid allocation

An effective document clustering system can significantly improve the tasks of document analysis, grouping, and retrieval. The performance of a document clustering system mainly depends on document preparation and allocation of cluster positions. As achieving optimal document clustering is a combinatorial NP-hard optimization problem, it becomes essential to utilize non-traditional methods to look for optimal or near-optimal solutions. During the allocation of cluster positions or the centroids allocation process, the extra text features that represent keywords in each document have an effect on the clustering results. A large number of features need to be reduced using dimensionality reduction techniques. Feature selection is an important step that can be used to reduce the redundant and inconsistent features. Due to a large number of the potential feature combinations, text feature selection is considered a complicated process. The persistent drawbacks of the current text feature selection methods such as local optima and absence of class labels of features were addressed in this thesis. The supervised and unsupervised feature selection methods were investigated. To address the problems of optimizing the supervised feature selection methods so as to improve document clustering, memetic hybridization between filter and wrapper feature selection, known as Memetic Algorithm Feature Selection, was presented first. In order to deal with the unlabelled features, unsupervised feature selection method was also proposed. The proposed unsupervised feature selection method integrates Simulated Annealing to the global search using Differential Evolution. This combination also aims to combine the advantages of both the wrapper and filter methods in a memetic scheme but on an unsupervised basis. Two versions of this hybridization were proposed. The first was named Differential Evolution Simulated Annealing, which uses the standard mutation of Differential Evolution, and the second was named Dichotomous Differential Evolution Simulated Annealing, which used the dichotomous mutation of the differential evolution. After feature selection two centroid allocation methods were proposed; the first is the combination of Chaotic Logistic Search and Discrete Differential Evolution global search, which was named Differential Evolution Memetic Clustering (DEMC) and the second was based on using the Gradient search using the k-means as a local search with a modified Differential Harmony global Search. The resulting method was named Memetic Differential Harmony Search (MDHS). In order to intensify the exploitation aspect of MDHS, a binomial crossover was used with it. Finally, the improved method is named Crossover Memetic Differential Harmony Search (CMDHS). The test results using the F-measure, Average Distance of Document to Cluster (ADDC) and the nonparametric statistical tests showed the superiority of the CMDHS over the baseline methods, namely the HS, DHS, k-means and the MDHS. The tests also show that CMDHS is better than the DEMC proposed earlier. Finally the proposed CMDHS was compared with two current state-of-the-art methods, namely a Krill Herd (KH) based centroid allocation method and an Artifice Bee Colony (ABC) based method, and found to outperform these two methods in most cases

Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

AI for social good: social media mining of migration discourse

The number of international migrants has steadily increased over the years, and it has become one of the pressing issues in today’s globalized world. Our bibliometric review of around 400 articles on Scopus platform indicates an increased interest in migration-related research in recent times but the extant research is scattered at best. AI-based opinion mining research has predominantly noted negative sentiments across various social media platforms. Additionally, we note that prior studies have mostly considered social media data in the context of a particular event or a specific context. These studies offered a nuanced view of the societal opinions regarding that specific event, but this approach might miss the forest for the trees. Hence, this dissertation makes an attempt to go beyond simplistic opinion mining to identify various latent themes of migrant-related social media discourse. The first essay draws insights from the social psychology literature to investigate two facets of Twitter discourse, i.e., perceptions about migrants and behaviors toward migrants. We identified two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users toward migrants. Additionally, this essay has also fine-tuned the binary hate speech detection task, specifically in the context of migrants, by highlighting the granular differences between the perceptual and behavioral aspects of hate speech. The second essay investigates the journey of migrants or refugees from their home to the host country. We draw insights from Gennep's seminal book, i.e., Les Rites de Passage, to identify four phases of their journey: Arrival of Refugees, Temporal stay at Asylums, Rehabilitation, and Integration of Refugees into the host nation. We consider multimodal tweets for this essay. We find that our proposed theoretical framework was relevant for the 2022 Ukrainian refugee crisis – as a use-case. Our third essay points out that a limited sample of annotated data does not provide insights regarding the prevailing societal-level opinions. Hence, this essay employs unsupervised approaches on large-scale societal datasets to explore the prevailing societal-level sentiments on YouTube platform. Specifically, it probes whether negative comments about migrants get endorsed by other users. If yes, does it depend on who the migrants are – especially if they are cultural others? To address these questions, we consider two datasets: YouTube comments before the 2022 Ukrainian refugee crisis, and during the crisis. Second dataset confirms the Cultural Us hypothesis, and our findings are inconclusive for the first dataset. Our final or fourth essay probes social integration of migrants. The first part of this essay probed the unheard and faint voices of migrants to understand their struggle to settle down in the host economy. The second part of this chapter explored the viability of social media platforms as a viable alternative to expensive commercial job portals for vulnerable migrants. Finally, in our concluding chapter, we elucidated the potential of explainable AI, and briefly pointed out the inherent biases of transformer-based models in the context of migrant-related discourse. To sum up, the importance of migration was recognized as one of the essential topics in the United Nation’s Sustainable Development Goals (SDGs). Thus, this dissertation has attempted to make an incremental contribution to the AI for Social Good discourse

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

On Improving (Non)Functional Testing

Software testing is commonly classified into two categories, nonfunctional testing and functional testing. The goal of nonfunctional testing is to test nonfunctional requirements, such as performance and reliability. Performance testing is one of the most important types of nonfunctional testing, one goal of which is to detect the phenomena that an Application Under Testing (AUT) exhibits unexpectedly worse performance (e.g., lower throughput) with some input data. During performance testing, a critical challenge is to understand the AUT’s behaviors with large numbers of combinations of input data and find the particular subset of inputs leading to performance bottlenecks. However, enumerating those particular inputs and identifying those bottlenecks are always laborious and intellectually intensive. In addition, for an evolving software system, some code changes may accidentally degrade performance between two software versions, it is even more challenging to find problematic changes (out of a large number of committed changes) may lead to performance regressions under certain test inputs. This dissertation presents a set of approaches to automatically find specific combinations of input data for exposing performance bottlenecks and further analyze execution traces to identify performance bottlenecks. In addition, this dissertation also provides an approach that automatically estimates the impact of code changes on performance degradation between two released software versions to identify the problematic ones likely leading to performance regressions. Functional testing is used to test the functional correctness of AUTs. Developers commonly write test suites for AUTs to test different functionalities and locate functional faults. During functional testing, developers rely on some strategies to order test cases to achieve certain objectives, such as exposing faults faster, which is known as Test Case Prioritization (TCP). TCP techniques are commonly classified into two categories, dynamic and static techniques. A set of empirical studies has been conducted to examine and understand different TCP techniques, but there is a clear gap in existing studies. No study has compared static techniques against dynamic techniques and comprehensively examined the impact of test granularity, program size, fault characteristics, and the similarities in terms of fault detection on TCP techniques. Thus, this dissertation presents an empirical study to thoroughly compare static and dynamic TCP techniques in terms of effectiveness, efficiency, and similarity of uncovered faults at different granularities on a large set of real-world programs, and further analyze the potential impact of program size and fault characteristics on TCP evaluation. Moreover, in the prior work, TCP techniques have been typically evaluated against synthetic software defects, called mutants. For this reason, it is currently unclear whether TCP performance on mutants would be representative of the performance achieved on real faults. to answer this fundamental question, this dissertation presents the first empirical study that investigates TCP performance when applied to both real-world faults and mutation faults for understanding the representativeness of mutants