Geok See Ng

A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure

Abstract Early detection of breast cancer is the key to improve survival rate. Thermogram is a promising front-line screening tool as it is able to warn women of breast cancer up to 10 years in advance. However, analysis and interpretation of thermogram are heavily dependent on the analysts, which may be inconsistent and error-prone. In order to boost the accuracy of preliminary screening using thermogram without incurring additional financial burden, Complementary Learning Fuzzy Neural Network (CLFNN), FALCON-AART is proposed as the Computer-Assisted Intervention (CAI) tool for thermogram analysis. CLFNN is a neuroscience-inspired technique that provides intuitive fuzzy rules, human-like reasoning, and good classification performance. Confluence of thermogram and CLFNN offers a promising tool for fighting breast cancer.

GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms

Fuzzy logic allows mapping of an input space to an output space. The mechanism for doing this is through a set of IF-THEN statements, commonly known as fuzzy rules. In order for a fuzzy rule to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this approach is the difficulty of automatically and accurately constructing the membership functions. Genetic Algorithms (GAs) is a technique that emulates biological evolutionary theories to solve complex optimization problems. Genetic Algorithms provide an alternative to our traditional optimization techniques by using directed random searches to derive a set of optimal solutions in complex landscapes. GAs literally searches towards the two end of the search space in order to determine the optimum solutions. Populations of candidate solutions are evaluated to determine the best solution. In this paper, a hybrid system combining a Fuzzy Inference System and Genetic Algorithms-a Genetic Algorithms based Takagi-Sugeno-Kang Fuzzy Neural Network (GA-TSKfnn) is proposed to tune the parameters in the Takagi-Sugeno-Kang fuzzy neural network. The aim is to reduce unnecessary steps in the parameters sets before they can be fed into the network. Modifications are made to various layers of the network to enhance the performance. The proposed GA-TSKfnn is able to achieve higher classification rate when compared against traditional neuro-fuzzy classifiers.

A novel single-pass thinning algorithm and an effective set of performance criteria

A new sequential thinning algorithm, which uses both flag map and bitmap simultaneously to decide if a boundary pixel can be deleted, as well as the incorporation of smoothing templates to smooth the final skeleton, is proposed in this paper. Three performance measurements are proposed for an objective evaluation of this novel algorithm against a set of well established techniques. Extensive result comparison and analysis are presented in this paper for discussion.

A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System

There are two important issues in neuro-fuzzy modeling: (1) interpretabilitythe ability to describe the behavior of the system in an interpretable wayand (2) accuracythe ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.

Ovarian cancer diagnosis with complementary learning fuzzy neural network

DNA microarray is an emerging technique in ovarian cancer diagnosis. However, very often, microarray data is ultra-huge and difficult to analyze. Thus, it is desirable to utilize fuzzy neural network (FNN) approach for assisting the diagnosis and analysis process. Amongst FNN, complementary learning FNN is able to rapidly derive fuzzy sets and formulate fuzzy rules. Complementary learning FNN uses positive and negative learning, and hence it subsides the effect of curse of dimension and is capable of modeling the dynamics of problem space with relative good classification performance. Furthermore, FALCON-AART has human-like reasoning that allows physician to examine its computation in a familiar way. FALCON-AART can generate intuitive fuzzy rule to justify its reasoning, which is important to generate trust among the users of the system. Hence, FALCON-AART is applied in ovarian cancer diagnosis as a clinical decision support system in this work. Its experimental results are encouraging.

FCMAC-EWS: A bank failure early warning system based on a novel localized pattern learning and semantically associative fuzzy neural network

In the banking industry, it is highly desirable to identify potential bank failure or high-risk banks. Successful early warning systems (EWS) would provide capabilities to avoid adverse financial repercussions and a massive bail out costs for the failing banks. Very often, these failures are due to financial distress. Various traditional statistical models have been used to study failures of financial institutions (Sinkey, J., Jr. (1975). A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance, 1, 21-36; Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249-276; Lane, W., Looney, S., & Wansley, J. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance, 10, 511-531; Cole, R., & Gunther, J. (1995). Separating the likelihood and timing of bank failure. Journal of Banking and Finance, 19, 1073-1089.). However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes a novel fuzzy CMAC (cerebellar model articulation controller) model based on compositional rule of inference, named FCMAC-CRI(S), as a new approach to tackle the problem using localized learning. The CRI-based FCMAC network, based on localized training, is able to identify the inherent traits and patterns of financial distress based on financial covariates derived from publicly available financial statements. The use of localized learning is akin to the neocortex semantic associative memory which is superior to the hippocampal form of global learning. The reason is that the hippocampal memory system rapidly learns arbitrary patterns of activity, whereas the neocortical system learns slowly. The slow learning of the neocortex is a requirement for any system that is able to eventually extract and model the similarity structure in its environment. The rapid learning of the hippocampal system, in contrast, sacrifices the ability to generalize. When both systems are intact, the hippocampal memory system trains the neocortical learning system through a process of repeated patterns, allowing for the gradual extraction of the similarity structure that is central to generalization. In FCMAC-CRI(S), its interactive relations among the selected pattern features are captured in the form of highly intuitive fuzzy IF-THEN rules, which form the knowledge base of the early warning system and provide insights into the characteristics of financial distress. The performance of the FCMAC-CRI(S) is benchmarked against that of the Coxs proportional hazard model and GenSoFNN-CRI(S) network, a functional hippocampal fuzzy semantic learning memory structure, in predicting bank failures based on a population of 3635 US banks observed over 21 years. The localized models and learning yield superior results and interpretation to fuzzy neural network such as GenSoFNN-CRI(S) that are based on global learning. The performance of the new approach as a bank failure classification and early warning system is highly encouraging.

2005 Special Issue: Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures

Early detection and accurate staging of ovarian cancer are the keys to improving survival rate. However, at present there is no single diagnosis modality that is sufficiently sensitive. DNA microarray analysis is an emerging technique that has potential for ameliorating the hardship in early detection and staging of ovarian disease. However, microarray data is ultra-huge and difficult to analyze. Hence, computational intelligence methods are often utilized to assist in the diagnosis and analysis process. Fuzzy Neural Networks (FNN) are more suitable for this task as FNN provides not only the accuracy, but also the interpretability of its reasoning process. Hippocampus-inspired Complementary Learning FNN (CLFNN) is able to rapidly derive fuzzy sets and formulate fuzzy rules. CLFNN uses positive and negative learning, and hence it reduces the effect of the curse of dimensionality and is capable of modeling the dynamics of the problem space with relatively good classification performance. One of its successors, a hybrid of complementary hippocampal learning and associative neocortical learning called Pseudo Associative Complementary Learning (PACL), is a structure that seeks to functionally model the memory consolidation process. Both PACL and CLFNN have human-like reasoning that allows physicians to examine their computation using familiar terms. They can construct intuitive fuzzy rules autonomously to justify their reasoning, which is important to generate trust among the users. Hence, CLFNN and PACL are applied as a diagnostic decision support system in ovarian cancer diagnosis. The experimental results are encouraging.

Novel Self-Organizing Takagi Sugeno Kang Fuzzy Neural Networks Based on ART-like Clustering

The existing Self-Organizing Takagi Sugeno Kang Fuzzy Neural Networks (S-TSKfnn) structure uses virus infection clustering (VIC) method to generate fuzzy rules. In this paper, we propose a novel architecture called Modified S-TSKfnn (MS-TSKfnn) that uses ART-like clustering called discrete incremental clustering (DIC). By doing so, MS-TSKfnn is able to handle online data input, and its performance is also enhanced. Most importantly, the accurate clustering in the fuzzy set derivation has significantly reduced the number of fuzzy TSK rules necessary to describe a problem. Extensive simulations are conducted using MS-TSKfnn and its performance is encouraging when benchmarked against other established neuro-fuzzy systems. The empirical work also firmly demonstrated the importance of clustering within a fuzzy neural reasoning system in ensuring a compact and expressive fuzzy rate base.

BIOLOGICAL BRAIN-INSPIRED GENETIC COMPLEMENTARY LEARNING FOR STOCK MARKET AND BANK FAILURE PREDICTION

Genetic complementary learning (GCL) is a biological brain‐inspired learning system based on human pattern recognition, and genes selection process. It is a confluence of the hippocampal complementary learning and the evolutionary genetic algorithm. With genetic algorithm providing the possibility of optimal solution, and complementary learning providing the efficient pattern recognition, GCL may offer superior performance. In contrast to other computational finance tools such as neural network and statistical methods, GCL provides greater interpretability and it does not rely on the assumption of the underlying data distribution. It is an evolving and autonomous system that avoids the time‐consuming process of manual rule construction or modeling. This is highly favorable especially in financial world where data is ever changing, and requires frequent update. The feasibility of GCL as stock market predictor, and bank failure early warning system is investigated. The experimental results show that GCL is a competent computational finance tools for stock market prediction and bank failure early warning system.

Brain-inspired genetic complementary learning for stock market prediction

Traditional technical analysis for stock market prediction is error-prone, especially for multiyear trend prediction. Hence, computational intelligence provides an attractive alternative. Among the plethora of methods, statistics and artificial neural network are the most popular. However, they are black boxes that are not interpretable. Genetic complementary learning (GCL) fuzzy neural network is therefore proposed. GCL is a brain-inspired learning algorithm that is a confluence of genetic algorithm (GA) and hippocampal complementary learning. Since GA has the potential of finding optimal solution, and complementary learning is one of the mechanisms underlying human recognition, GCL may offer superior performance. The experimental results have demonstrated that GCL is a competent stock market prediction system.