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Creating an applicable and precise failure prediction system is highly desirable for decision makers and regulators in the finance industry. This study develops a new Failure Prediction (FP) approach which effectively integrates a fuzzy logic-based adaptive inference system with the learning ability of a neural network to generate knowledge in the form of a fuzzy rule base. This FP approach uses a...
Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of...
Data mining or knowledge discovery in databases in simple words is the non-trivial extraction of implicit, previously unknown and potentially useful information from data. It deals with the discovery of hidden knowledge, unexpected patterns and new rules from large databases. Knowledge discovery in databases is the process of identifying a valid, potentially useful and ultimately understandable structure...
Feature selection is a most important procedure which can affect the performance of pattern recognition systems. Since most feature selection algorithms easily fall into local optimum, a novel ant colony optimization approach to feature selection based on fuzzy entropy is proposed (ACOFE). In the proposed algorithm, fuzzy entropy is adopted as pheromone information for ant colony optimization. In...
Genetic fuzzy rule selection has been successfully used to design accurate and interpretable fuzzy classifiers from numerical data. In our former study, we proposed its parallel distributed implementation which can drastically decrease the computational time by dividing both a population and a training data set into sub-groups. In this paper, we examine the effect of data reduction on the generalization...
This work presents a new process for building comprehensible fuzzy systems for classification problems. Firstly, a feature selection procedure based on crisp decision trees is carried out. Secondly, strong fuzzy partitions are generated for all the selected inputs. Thirdly, a set of linguistic rules are defined combining the previously generated linguistic variables. Then, a linguistic simplification...
This paper presents an approach based on Projection Pursuit and fuzzy rule extraction combining new hybrid method of classification system. This method is the first to use projection pursuit technology to deal with training set of sample dimensionality reduction and in accordance with the sample classification. According to the results of the classification and the best value projection, using trapezoid...
In authors' previous works, a novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) was proposed. SONeFMUC is composed of small-scale interconnected fuzzy neuron classifiers (FNCs) arranged in layers. The structure of the classifier is revealed by means of the well known GMDH algorithm. In addition, the GMDH algorithm inherently implements feature selection, considering the most informative...
We focus on extracting rules from a trained FAMR model. The FAMR is a fuzzy ARTMAP (FAM) incremental learning system used for classification, probability estimation, and function approximation. The set of rules generated is post-processed in order to improve its generalization capability. Our method is suitable for small training sets. We compare our method with another neuro-fuzzy algorithm, and...
Textual Entailment (TE) recognition is a task which consists in recognizing if a textual expression, the text T, entails another expression, the hypothesis H. Recently it is treated as a common solution for modeling language variability. Textual entailment captures a broad range of semantic oriented inferences needed for many Natural Language Processing (NLP) applications, like Information Retrieval...
The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using artificial immune system methods. Accuracy boosting relies on fuzzy partition learning. The modified algorithm was experimentally proved to be more accurate for all learning sets containing non-crisp attributes.
In this paper, first we discuss human motion analysis using the temporal template methodology. This methodology deals with the creation of motion history images (MHIs). Hu moment invariants are calculated from MHIs, for feature description. Two types of training datasets based on Hu moment invariants, have been developed. One training dataset is of 105times7 elements and other consists of 200times7...
In order to construct intelligible and effective land evaluation classifier, a semi-supervised learning algorithm constructed by utilizing simplified association rules combining with k-mean clustering algorithm is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of association rules further, an algorithm to eliminate redundant...
In this paper, we propose an approach to complexity reduction of Mamdani-type fuzzy rule-based systems (FRBSs) based on removing logical redundancies. We first generate an FRBS from data by applying a simplified version of the well-known Wang and Mendel method. Then, we represent the FRBS as a multi-valued logic relation. Finally, we apply MVSIS, a tool for circuit minimization and simulation, to...
Reinforcement learning (RL) is learning how to map states to actions so as to maximise a numeric reward signal. Fuzzy Q-learning (FQL) extends the RL technique Q-learning to large or continuous problems and has been applied to a wide range of applications from data mining to robot control. Typically, FQL uses a uniform or pre-defined internal representation provided by the human designer. A uniform...
Due to the learning problem on skewed distribution datasets, which tend to produce high accuracy over the majority class but poor predictive accuracy over the minority class by traditional machine learning algorithms, fuzzy information granulation based knowledge discovery and decision support model called FIG mode is proposed in this paper to improve classification performance and make effective...
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