Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification

Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM. Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM dalam menerajui tugas-tugas pengelasan corak. ________________________________________________________________________________________________________________________ Pattern classification is one of the primary issues in various data mining tasks. In this study, the main research focus is on the design and development of hybrid models, combining the supervised Adaptive Resonance Theory (ART) neural network and Reinforcement Learning (RL) models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM) network and Q-learning are adopted as the backbone for designing and developing the hybrid models. A new QFAM model is first introduced to improve the classification performance of FAM network. A pruning strategy is incorporated to reduce the complexity of QFAM. To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide predictions with explanations using only a few antecedents. To further improve the robustness of QFAM-based models, the notion of multi agent systems is employed. As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. A variety of benchmark problems are used for evaluation of individual and ensemble QFAM-based models. The results are analyzed and compared with those from FAM as well as other models reported in the literature. In addition, two real-world problems are used to demonstrate the practicality of the hybrid models. The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks

Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 1)

School attendance is an important foundational competency for children and adolescents, and school absenteeism has been linked to myriad short- and long-term negative consequences, even into adulthood. Many efforts have been made to conceptualize and address this population across various categories and dimensions of functioning and across multiple disciplines, resulting in both a rich literature base and a splintered view regarding this population. This article (Part 1 of 2) reviews and critiques key categorical and dimensional approaches to conceptualizing school attendance and school absenteeism, with an eye toward reconciling these approaches (Part 2 of 2) to develop a roadmap for preventative and intervention strategies, early warning systems and nimble response, global policy review, dissemination and implementation, and adaptations to future changes in education and technology. This article sets the stage for a discussion of a multidimensional, multi-tiered system of supports pyramid model as a heuristic framework for conceptualizing the manifold aspects of school attendance and school absenteeism

Towards a consolidation of worldwide journal rankings - A classification using random forests and aggregate rating via data envelopment analysis

AbstractThe question of how to assess research outputs published in journals is now a global concern for academics. Numerous journal ratings and rankings exist, some featuring perceptual and peer-review-based journal ranks, some focusing on objective information related to citations, some using a combination of the two. This research consolidates existing journal rankings into an up-to-date and comprehensive list. Existing approaches to determining journal rankings are significantly advanced with the application of a new classification approach, ‘random forests’, and data envelopment analysis. As a result, a fresh look at a publication׳s place in the global research community is offered. While our approach is applicable to all management and business journals, we specifically exemplify the relative position of ‘operations research, management science, production and operations management’ journals within the broader management field, as well as within their own subject domain

Students Success Modeling: Most Important Factors

The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. The present study, motivated by the same encouragement, proposes a deep learning model trained with 121 features of diverse categories extracted or engineered out of the records of 60,822 postsecondary students. The model undertakes to identify students likely to graduate, the ones likely to transfer to a different school, and the ones likely to drop out and leave their higher education unfinished. This study undertakes to adjust its predictive methods for different stages of curricular progress of students. The temporal aspects introduced for this purpose are accounted for by incorporating layers of LSTM in the model. Our experiments demonstrate that distinguishing between to-be-graduate and at-risk students is reasonably achievable in the earliest stages, and then it rapidly improves, but the resolution within the latter category (dropout vs. transfer) depends on data accumulated over time. However, the model remarkably foresees the fate of students who stay in the school for three years. The model is also assigned to present the weightiest features in the procedure of prediction, both on institutional and student levels. A large, diverse sample size along with the investigation of more than one hundred extracted or engineered features in our study provide new insights into variables that affect students success, predict dropouts with reasonable accuracy, and shed light on the less investigated issue of transfer between colleges. More importantly, by providing individual-level predictions (as opposed to school-level predictions) and addressing the outcomes of transfers, this study improves the use of ML in the prediction of educational outcomes.Comment: 15 pages, 17 figures, 1 apendi

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival

Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources

Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence ex pected to rise over the next decades. Unconventional strategies for elucidating the genetic mechanisms are necessary due to its polygenic nature. In this work, the input information sources are five: a public DNA microarray that measures expression levels of control and patient samples, repositories of known genes associated to Alzheimer’s disease, additional data, Gene Ontology and finally, a literature review or expert knowledge to validate the results. As methodology to identify genes highly related to this disease, we present the integration of three machine learning techniques: particularly, we have used decision trees, quantitative association rules and hierarchical cluster to analyze Alzheimer’s disease gene expres sion profiles to identify genes highly linked to this neurodegenerative disease, through changes in their expression levels between control and patient samples. We propose an ensemble of decision trees and quantitative association rules to find the most suitable configurations of the multi-objective evolutionary algorithm GarNet, in order to overcome the complex parametrization intrinsic to this type of algorithms. To fulfill this goal, GarNet has been executed using multiple configuration settings and the well-known C4.5 has been used to find the minimum accuracy to be satisfied. Then, GarNet is rerun to identify de pendencies between genes and their expression levels, so we are able to distinguish between healthy individuals and Alzheimer’s patients using the configurations that overcome the minimum threshold of accuracy defined by C4.5 algorithm. Finally, a hierarchical cluster analysis has been used to validate the obtained gene-Alzheimer’s Disease associations provided by GarNet. The results have shown that the ob tained rules were able to successfully characterize the underlying information, grouping relevant genes for Alzheimer Disease. The genes reported by our approach provided two well defined groups that per fectly divided the samples between healthy and Alzheimer’s Disease patients. To prove the relevance of the obtained results, a statistical test and gene expression fold-change were used. Furthermore, this rel evance has been summarized in a volcano plot, showing two clearly separated and significant groups of genes that are up or down-regulated in Alzheimer’s Disease patients. A biological knowledge integration phase was performed based on the information fusion of systematic literature review, enrichment Gene Ontology terms for the described genes found in the hippocampus of patients. Finally, a validation phase with additional data and a permutation test is carried out, being the results consistent with previous studies.Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02Ministerio de Ciencia y Tecnología TIN2014-55894-C2-1-RJunta de Andalucía P11-TIC-752

Tree-based machine learning methods for survey research

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys

Challenge and Research Trends of Forecasting Financial Energy

The measurement of economic entities' financial strength is one of the significant challenges of modern economic and financial research. With increased financial globalization, faster economic changes, and a new dimension of increased financial risk in the context of the COVID-19 pandemic crisis due to its biological nature and broad scope, affecting the whole world simultaneously, the issue of forecasting financial energy is gaining much more importance currently. This Special Issue entitled „Challenge and Research Trends of Forecasting Financial Energy” is devoted to the broad research area of forecasting financial energy of economic units such as enterprises, households, local governments, etc. Conceptualizing the term of financial energy, we aim to capture a wide spectrum of predicting and evaluating the financial standing, including various aspects of corporate finance, personal finance, and public finance

Uncovering Instabilities in Variational-Quantum Deep Q-Networks

Deep Reinforcement Learning (RL) has considerably advanced over the past decade. At the same time, state-of-the-art RL algorithms require a large computational budget in terms of training time to converge. Recent work has started to approach this problem through the lens of quantum computing, which promises theoretical speed-ups for several traditionally hard tasks. In this work, we examine a class of hybrid quantum-classical RL algorithms that we collectively refer to as variational quantum deep Q-networks (VQ-DQN). We show that VQ-DQN approaches are subject to instabilities that cause the learned policy to diverge, study the extent to which this afflicts reproduciblity of established results based on classical simulation, and perform systematic experiments to identify potential explanations for the observed instabilities. Additionally, and in contrast to most existing work on quantum reinforcement learning, we execute RL algorithms on an actual quantum processing unit (an IBM Quantum Device) and investigate differences in behaviour between simulated and physical quantum systems that suffer from implementation deficiencies. Our experiments show that, contrary to opposite claims in the literature, it cannot be conclusively decided if known quantum approaches, even if simulated without physical imperfections, can provide an advantage as compared to classical approaches. Finally, we provide a robust, universal and well-tested implementation of VQ-DQN as a reproducible testbed for future experiments.Comment: Authors Maja Franz, Lucas Wolf, Maniraman Periyasamy contributed equally (name order randomised). To be published in the Journal of The Franklin Institut