Advances in fuzzy rule-based system for pattern classification

Ph.DDOCTOR OF PHILOSOPH

Prototype based clustering in high-dimensional feature spaces

...In dieser Arbeit untersuche ich den ”Fluch der Dimensionen” mittels dem Begriff der Distanzkonzentration. Ich zeige, dass dieser Effekt im Datenmodell mittels der paarweisen Kovarianzkoeffizienten der Randverteilungen beschrieben werden kann. Zusätzlich vergleiche ich 10 prototypbasierte Clusteralgorithmen mittels 800.000 Clusterergebnissen von künstlich erzeugten Datensätzen. Ich erforsche, wie und warum Clusteralgorithmen von der Anzahl der Merkmale beeinflusst werden. Mit den Clusterergebnissen untersuche ich außerdem, wie gut 5 der populärsten Clusterqualitätsmaße die tatsächliche Clusterqualität schätzen.Magdeburg, Univ., Fak. für Informatik, Diss., 2015von Roland Winkle

Enhancing the Prediction of Missing Targeted Items from the Transactions of Frequent, Known Users

The ability for individual grocery retailers to have a single view of its customers across all of their grocery purchases remains elusive, and is considered the “holy grail” of grocery retailing. This has become increasingly important in recent years, especially in the UK, where competition has intensified, shopping habits and demographics have changed, and price sensitivity has increased. Whilst numerous studies have been conducted on understanding independent items that are frequently bought together, there has been little research conducted on using this knowledge of frequent itemsets to support decision making for targeted promotions. Indeed, having an effective targeted promotions approach may be seen as an outcome of the “holy grail”, as it will allow retailers to promote the right item, to the right customer, using the right incentives to drive up revenue, profitability, and customer share, whilst minimising costs. Given this, the key and original contribution of this study is the development of the market target (mt) model, the clustering approach, and the computer-based algorithm to enhance targeted promotions. Tests conducted on large scale consumer panel data, with over 32000 customers and 51 million individual scanned items per year, show that the mt model and the clustering approach successfully identifies both the best items, and customers to target. Further, the algorithm segregates customers into differing categories of loyalty, in this case it is four, to enable retailers to offer customised incentives schemes to each group, thereby enhancing customer engagement, whilst preventing unnecessary revenue erosion. The proposed model is compared with both a recently published approach, and the cross-sectional shopping patterns of the customers on the consumer scanner panel. Tests show that the proposed approach outperforms the other approach in that it significantly reduces the probability of having “false negatives” and “false positives” in the target customer set. Tests also show that the customer segmentation approach is effective, in that customers who are classed as highly loyal to a grocery retailer, are indeed loyal, whilst those that are classified as “switchers” do indeed have low levels of loyalty to the selected grocery retailer. Applying the mt model to other fields has not only been novel but yielded success. School attendance is improved with the aid of the mt model being applied to attendance data. In this regard, an action research study, involving the proposed mt model and approach, conducted at a local UK primary school, has resulted in the school now meeting the required attendance targets set by the government, and it has halved its persistent absenteeism for the first time in four years. In medicine, the mt model is seen as a useful tool that could rapidly uncover associations that may lead to new research hypotheses, whilst in crime prevention, the mt value may be used as an effective, tangible, efficiency metric that will lead to enhanced crime prevention outcomes, and support stronger community engagement. Future work includes the development of a software program for improving school attendance that will be offered to all schools, while further progress will be made on demonstrating the effectiveness of the mt value as a tangible crime prevention metric

A Mathematical Measurement For Korean Text Mining and Its Application

Department of Mathematical SciencesIn modern society we are buried beneath an overwhelming amount of text data on the internet. We are less inclined to just surf the web and pass the time. To solve this problem, especially to grasp part and parcel of the text data we are presented, there have been numerous studies on the relationship between text data and the ease of the perception of the text???s meaning. However, most of the studies focused on English text data. Since most research did not take into account the linguistic characters, these same methods are not suitable for Korean text. Some special method is required to analyze Korean text data utilizing the characteristics of Korean. Thus we are proposing a new framework for Korean text mining in various texts via proper mathematical measurements. The framework is constructed with three parts: 1) text summarization 2) text clustering 3) relational text learning. Text summarization is the method of extracting the essential sentences from the text. As a measure of importance, we propose specific formulas which focus on the characteristics of Korean. These formulas will provide the input features for the fuzzy summarization system. However, this method has a significant defect for large data set. The number of the summarized sentences increases with the word count of a particular text. To solve this, we propose using text clustering. This field has been studied for a long time. It has a tradeo??? of accuracy for speed. Considering the syllable features of Asian linguistics, we have designed ???Syllable Vector??? as a new measurement. It has shown remarkable performance as implemented with text clustering, especially for high accuracy and speed through e???ectively reducing dimensions. Thirdly, we considered the relational feature of text data. The above concepts deal with the document itself. That is, text information has an independent relationship between documents. To handle these relations, we designed a new architecture for text learning using neural networks (NN). Recently, the most remarkable work in natural language processing (NLP) is ???word2vec???, which is built with artificial neural networks. Our proposed model has a learning structure of bipartite layers using meta information between text data, with a focus on citation relationships. This structure reflects the latent topic of the text using the quoted information. It can solve the shortcomings of the conventional system based on the term-document matrix.ope

Análise Dinâmica das Emoções através da Inteligência Artificial

Emotions have been demonstrated to be an important aspect of human intelligence and to play a significant role in human decision-making processes. Emotions are not only feelings but also processes of establishing, maintaining or disrupting the relation between the organism and the environment. In the present paper, several features of social and developmental Psychology are introduced, especially concepts that are related to Theories of Emotions and the Mathematical Tools applied in psychology (i.e., Dynamic Systems and Fuzzy Logic). Later, five models that infer emotions from a single event, in AV-Space, are presented and discussed along with the finding that fuzzy logic can measure human emotional states.Se ha comprobado que las emociones son un aspecto importante en la inteligencia humana y que desempeñan un rol significativo en el proceso humano de toma de decisiones. Las emociones no son solo sentimientos, sino también procesos de establecimiento, mantenimiento o interrupción de la relación existente entre el organismo y el ambiente. En el presente trabajo se describen algunas características de la psicología social y del desarrollo, especialmente los conceptos relacionados con las emociones y las teorías de la emoción, así como las herramientas matemáticas aplicadas en la psicología (i. e., sistemas dinámicos y lógica difusa). Luego se presentan y se discuten cinco modelos que infieren la emoción a partir de un evento, en el espacio Arousal-Valence (A-V), para encontrar que es posible usar la lógica difusa para medir los estados emocionales humanos.Se tem comprovado que as emoções são um aspeto importante na inteligência humana e que desempenha um papel significativo no processo de tomada de decisões humano. As emoções não são só sentimentos, mas também processos de estabelecimento, manutenção ou interrupção da relação existente entre o organismo e o ambiente. No presente trabalho descrevem-se algunas características da psicologia social e do desenvolvimento, especialmente os conceitos relacionados com emoções e as teorias da Emoção e as ferramentas matemáticas aplicadas na Psicologia (i.e., Sistemas dinámicos y Lógica difusa). Após, se apresentam e se discutem cinco modelos que inferem a emoção a partir de um evento, no espaço Arousal-Valence (A-V), encontrando que a lógica difusa pode usar-se para medir os estados emocionais humanos

Phenotyping Risk Profiles of Substance Use and Exploring the Dynamic Transitions in Use Patterns: Machine Learning Models using the COMPASS Data

Background Polysubstance use is on the rise among Canadian youth. Examining risk profiles and understanding how the transition occurs in use patterns can inform the design and implementation of polysubstance risk reduction intervention. The COMPASS study is longitudinal research examining health-related behaviours among Canadian secondary school students, capturing data from multiple sources. Machine learning (ML) techniques can reveal non-linearity and multivariate couplings associated with population-level longitudinal data to inform public health policies. Objectives The overarching goal of this thesis is to identify phenotypes of risk profiles of youth polysubstance use and examine the dynamic transitions of use patterns across time, utilizing both unsupervised ML methods and a latent variable modelling approach. This thesis also aims to understand how ML techniques are best used in modelling transitions and discovering the “hidden” patterns from large complex population-based health survey data, using the COMPASS dataset as a showcase. Methods A linked sample (N = 8824) of three annual waves of the COMPASS data collected starting from the school year of 2016-17 was used. Multiple imputations for missing values were performed. Substance use indicators, including cigarette smoking, e-cigarette use, alcohol drinking, and marijuana consumption, were categorized into “never use,” “occasional use,” and “current use.” To examine phenotypes of risk profiles, hierarchical clustering, partitioning around medoids (PAM), and fuzzy clustering algorithms were applied. The Boruta algorithm was used to identify a subset of features for cluster analysis. Both the internal and external indices were employed to evaluate the clustering validity. A multivariate latent Markov model (LMM) was implemented to explore the dynamic transitions of use patterns over time. The least absolute shrinkage and selection operator (LASSO) approach was applied to select the appropriate covariates for entering the LMM. Model selection was based on the Bayesian information criterion (BIC) and the goodness-of-fit test. Results The top factors impacting youth polysubstance use included the number of smoking friends, the number of skipped classes, the weekly money to spend/save oneself, and others. Four risk profiles of polysubstance use were identified across the three waves: low, medium-low, medium-high, and high-risk profiles. The heterogeneity in the prevalence and phenotype across these four risk profiles was confirmed. The internal measures of clustering performance measured by average silhouette width ranged from 0.51 to 0.55 across the three waves using different clustering algorithms. The clustering algorithms achieved a relatively high degree of agreement on cluster membership. Comparing the fuzzy (FANNY) clustering with PAM clustering, the adjusted Rand indices were 0.9698, 0.7676, and 0.6452 for the three waves. Four distinct use patterns were identified: no use (S1), occasional single-use of alcohol (S2), dual-use of e-cigarette and alcohol (S3), and current multi-use (S4). The initial probabilities of each subgroup were 0.5887, 0.2156, 0.1487, and 0.0470. The marginal distribution of S1 decreased, while that of S3 and S4 increased over time, indicating a tendency towards increased substance use as the students grew older. Although, generally, most students remained in the same subgroup across time, particularly the individuals in S4 with the highest transition probability (0.8668). Over time, those who transitioned typically moved towards a more severe use pattern group, e.g., S3 -> S4. Factors that impact the initial membership of use patterns and the dynamic transitions were multifaceted and complex across the four use patterns across the three waves. Not only do use patterns change with time, but so does the evidence in use patterns. Conclusion As the first study of its kind to ascertain risk profiles and dynamics of use patterns in youth polysubstance use, by employing ML approaches to the COMPASS dataset, this thesis provides insights into the opportunities and possibilities ahead for ML in Public Health. Findings from this thesis can be beneficial to practitioners in the field, such as school program managers or policymakers, in their capacity to develop interventions to prevent or remedy polysubstance use among youth