Interpretable Recognition for Dementia Using Brain Images

Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.

Remote Sensing Image Classification with a Graph-Based Pre-Trained Neighborhood Spatial Relationship

Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification “smarter”. In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the “siphonic effect” produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.

Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion

A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity.

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.