Fuzzy transfer learning for financial early warning system

University of Technology, Sydney. Faculty of Engineering and Information Technology.Financial early warning system aims to warn of the impending critical financial status of an organization. A financial early warning system is more than a classical prediction model and should provide an explanatory analysis to describe the reasons behind the failure; the explanatory ability of a system is as important as its predictive accuracy. In addition, failure prediction is intrinsically a class imbalance problem in which the number of failed cases is much less than the number of survived cases. Also, the vagueness in the value of predictors is an inevitable problem which has emerged in the uncertain environment of the finance industry. Scarcity of training data is another critical problem in finance industry; a new type of financial early warning system, which can be transferred and modified for different domains to transfer knowledge to new prediction domain, is highly desirable in practical applications because it is easy to install and cheap to setup. To achieve the aforementioned properties, this study develops algorithms, methods and approaches in the case of bank failure prediction. First, a novel parametric adaptive inference-based fuzzy neural network approach is devised to predict financial status accurately and generate valuable knowledge for decision making. It handles the imbalance problem and the vagueness in features‘ value using parametric learning and rule generation algorithms. Second, a fuzzy domain adaptation method is developed to transfer knowledge from a related old problem to the problem under consideration and the labels are then predicted with a high level of accuracy. This method handles the data scarcity problem and enables the financial early warning system to be transferrable between prediction domains which are different in data distribution. Third, a fuzzy cross-domain adaptation approach is proposed to make the financial early warning system transferable from different but related domains to the current domain. This approach handles the problem in which the feature spaces of prediction domains are different and have vague value. This approach selects the significant fuzzy predictors in the current prediction domain by transferring knowledge from the related prediction domains. The proposed algorithms, methods and approaches are validated and benchmarked in each step of development using experiments performed on real world data. The results show that this study significantly enhances predictive accuracy at different stages of development. Finally a case study is performed to integrate and validate the proposed methods and approaches using Australian banking system data. The results demonstrate that this study successfully solves the abovementioned problems and significantly outperforms existing methods

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper

Multistep Fuzzy Bridged Refinement Domain Adaptation Algorithm and Its Application to Bank Failure Prediction

© 2015 IEEE. Machine learning plays an important role in data classification and data-based prediction. In some real-world applications, however, the training data (coming from the source domain) and test data (from the target domain) come from different domains or time periods, and this may result in the different distributions of some features. Moreover, the values of the features and/or labels of the datasets might be nonnumeric and involve vague values. Traditional learning-based prediction and classification methods cannot handle these two issues. In this study, we propose a multistep fuzzy bridged refinement domain adaptation algorithm, which offers an effective way to deal with both issues. It utilizes a concept of similarity to modify the labels of the target instances that were initially predicted by a shift-unaware model. It then refines the labels using instances that are most similar to a given target instance. These instances are extracted from mixture domains composed of source and target domains. The proposed algorithm is built on a basis of some data and refines the labels, thus performing completely independently of the shift-unaware prediction model. The algorithm uses a fuzzy set-based approach to deal with the vague values of the features and labels. Four different datasets are used in the experiments to validate the proposed algorithm. The results, which are compared with those generated by the existing domain adaptation methods, demonstrate a significant improvement in prediction accuracy in both the above-mentioned datasets

Text categorization by fuzzy domain adaptation

Machine learning methods have attracted attention of researches in computational fields such as classification/categorization. However, these learning methods work under the assumption that the training and test data distributions are identical. In some real world applications, the training data (from the source domain) and test data (from the target domain) come from different domains and this may result in different data distributions. Moreover, the values of the features and/or labels of the data sets could be non-numeric and contain vague values. In this study, we propose a fuzzy domain adaptation method, which offers an effective way to deal with both issues. It utilizes the similarity concept to modify the target instances' labels, which were initially classified by a shift-unaware classifier. The proposed method is built on the given data and refines the labels. In this way it performs completely independently of the shift-unaware classifier. As an example of text categorization, 20Newsgroup data set is used in the experiments to validate the proposed method. The results, which are compared with those generated when using different baselines, demonstrate a significant improvement in the accuracy. © 2013 IEEE

Synthesis And Anticancer Studies Of Methylene Bridged N-Heterocyclic Carbene Silver(i) And Palladium(ii) Complexes Derived From Imidazol-2-Ylidenes

Tesis ini menerangkan sintesis, pencirian (1H, 13C NMR, CHN, takat lebur) dan aktiviti kanser bagi titian metil garam bis-imidazolium (1-4) dan kompleks perak(I)- N-heterosiklik karbena (NHC). This thesis presents the synthesis, characterization (1H, 13C NMR, CHN, melting point) and anticancer activity of new methylene-bridged bis-imidazolium salts (1-4) and their respective silver(I)-N-heterocylic carbene (NHC) complexes

CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning

The industrialization has been the primary cause of the economic boom in almost all countries. However, this happened at the cost of the environment, as industrialization also caused carbon emissions to increase exponentially. According to the established literature, Gross Domestic Product (GDP) is related to carbon emissions (CO2) which could be optimally employed to precisely estimate a country's GDP. However, the scarcity of data is a significant bottleneck that could be handled using transfer learning (TL) which uses previously learned information to resolve new tasks, more specifically, related tasks. Notably, TL is highly vulnerable to performance degradation due to the deficiency of suitable information and hesitancy in decision-making. Therefore, this paper proposes ‘Intuitionistic Fuzzy Transfer Learning (IFTL)’, which is trained to use CO2 emission data of developed nations and is tested for its prediction of GDP in a developing nation. IFTL exploits the concepts of intuitionistic fuzzy sets (IFSs) and a newly introduced function called the modified Hausdorff distance function. The proposed IFTL is investigated to demonstrate its actual capabilities for TL in modeling hesitancy. To further emphasize the role of hesitancy modelled with IFSs, we propose an ordinary fuzzy set (FS) based transfer learning. The prediction accuracy of the IFTL is further compared with widely used machine learning approaches, extreme learning machines, support vector regression, and generalized regression neural networks. It is observed that IFTL capably ensured significant improvements in the prediction accuracy over other existing approaches whenever training and testing data have huge data distribution differences. Moreover, the proposed IFTL is deterministic in nature and presents a novel way for mathematically computing the intuitionistic hesitation degree.© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Mapping of the Chromium and Iron Pyrazolate Landscape

The main objective of this project is to synthesize the first family of polynuclear chromium pyrazolate complexes. Complexity in analysis of the experimental magnetic data of multinuclear complexes arises from their (2S +1)N microstates, where S is the spin of each metal center and N is the number of metal centers. For example, high-spin (HS)-FeIII3 has 216 microstates and HS-FeIII8 ≈ 1.7x106 microstates (S= 5/2). However, complexes with chromium(III) S = 3/2 will have a noticeable reduction of microstates. Mononuclear complexes with formula [mer-CrCl3(pzH*)3] (pz*H = pyrazole, 3-Me-pzH, 4-Me-pzH, 4-Cl-pzH, 4-I-pzH, 4-Br-pzH) and [trans-CrCl2(pzH*)4]Cl (pzH* = pyrazole and 3-Me-pzH) were synthesized and thoroughly characterized. Polynuclear iron pyrazolate complexes are prepared by the addition of base to [mer-FeCl3(pzH*)3] and [trans-FeCl2(pzH*)4]Cl complexes; the path is not paralleled by mononuclear chromium(III) pyrazole complexes. There is a challenging situation with these reactions, caused by the attainment of equilibrium, where the stable mononuclear complexes and traces of dinuclear species coexist in solution. Microwave assisted reaction of Cr(NO3)3·9H2O and pyrazole ligand in dimethylformamide (DMF) solution afforded redox inactive trinuclear formate-pyrazolate mixed-ligand complexes with formula [Cr3(μ3-O)(μ-O2CH)3(μ-4-R-pz)3(DMF)3]+ (pz = pyrazolate anion; R= H, Me, Cl). Thermally assisted synthesis with non-hydrolysable solvent yielded an electrochemically active all-pyrazolate complex. Complex with formula (Ph4P)2[Cr3(μ3-O)(μ-4-Cl-pz)6Cl3] and (Ph4P)2[Cr3(μ3-O)(μ-4-Cl-pz)6Br3] have an oxidation process at 0.502 V at 0.332 V, respectively. The latter has a second accessed oxidation process at 0.584 V. These systems are the first example of electrochemically amendable trinuclear pyrazolate complex with {Cr3O} core. The all-ferric complexes [Fe3(μ3-O)(μ-4-NO2-pz)6(L)3]2- (L = NCO-, N3) were synthesized from reaction of [Fe3(μ3-O)(μ-4-NO2-pz)6Cl3]2- with NaNCO and NaN3. Expected reversible reduction processes were observed for both complexes at more negative potential, -0.70 V, compared to the thiocyanate complex (-0.36 V). The 57Fe Mössbauer of the reduced [Fe3(μ3-O)(μ-4-NO2-pz)6(N3)3]3- is suggestive of a HS-to-LS electronic reorganization, as seen for the [Fe3(μ3-O)(μ-4-NO2-pz)6(SCN)3]3- complex. Furthermore, compound [Fe3(μ3-O)(μ-4-NO2-pz)6(N3)3]2-, shows a unique reversible oxidation process at 0.82 V (vs. Fc+/Fc) to a mixed-valent, formally Fe3+2/Fe4+ species