A fuzzy random forest

AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a fuzzy random forest, is proposed. This approach combines the robustness of multiple classifier systems, the power of the randomness to increase the diversity of the trees, and the flexibility of fuzzy logic and fuzzy sets for imperfect data management. Various combination methods to obtain the final decision of the multiple classifier system are proposed and compared. Some of them are weighted combination methods which make a weighting of the decisions of the different elements of the multiple classifier system (leaves or trees). A comparative study with several datasets is made to show the efficiency of the proposed multiple classifier system and the various combination methods. The proposed multiple classifier system exhibits a good accuracy classification, comparable to that of the best classifiers when tested with conventional data sets. However, unlike other classifiers, the proposed classifier provides a similar accuracy when tested with imperfect datasets (with missing and fuzzy values) and with datasets with noise

Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation

Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules

Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation

Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules

Fuzzy Decision Tree-based Inference System for Liver Disease Diagnosis

Medical diagnosis can be challenging because of a number of factors. Uncertainty in the diagnosis process arises from inaccuracy in the measurement of patient attributes, missing attribute data and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. Given this situation, a decision support system, which can help doctors come up with a more reliable diagnosis, can have a lot of potential. Decision trees are used in data mining for classification and regression. They are simple to understand and interpret as they can be visualized. But, one of the disadvantages of decision tree algorithms is that they deal with only crisp or exact values for data. Fuzzy logic is described as logic that is used to describe and formalize fuzzy or inexact information and perform reasoning using such information. Although both decision trees and fuzzy rule-based systems have been used for medical diagnosis, there have been few attempts to use fuzzy decision trees in combination with fuzzy rules. This study explored the application of fuzzy logic to help diagnose liver diseases based on blood test results. In this project, inference systems aimed at classifying patient data using a fuzzy decision tree and a fuzzy rule-based system were designed and implemented. Fuzzy decision tree was used to generate rules that formed the rule-base for the diagnostic inference system. Results from this study indicate that for the specific patient data set used in this experiment, the fuzzy decision tree-based inferencing out performed both the crisp decision tree and the fuzzy rule-based inferencing in classification accuracy

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve

Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?

To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy min-max neural network with its basic learning procedure is used within six different algorithm independent learning schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions examined is whether and when to use a single classifier or a combination of a number of component classifiers within a multiple classifier system

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided

Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability

In this paper a combination of neuro-fuzzy classifiers for improved classification performance and reliability is considered. A general fuzzy min-max (GFMM) classifier with agglomerative learning algorithm is used as a main building block. An alternative approach to combining individual classifier decisions involving the combination at the classifier model level is proposed. The resulting classifier complexity and transparency is comparable with classifiers generated during a single crossvalidation procedure while the improved classification performance and reduced variance is comparable to the ensemble of classifiers with combined (averaged/voted) decisions. We also illustrate how combining at the model level can be used for speeding up the training of GFMM classifiers for large data sets