41,984 research outputs found
Terrorism Event Classification Using Fuzzy Inference Systems
Terrorism has led to many problems in Thai societies, not only property
damage but also civilian casualties. Predicting terrorism activities in advance
can help prepare and manage risk from sabotage by these activities. This paper
proposes a framework focusing on event classification in terrorism domain using
fuzzy inference systems (FISs). Each FIS is a decision-making model combining
fuzzy logic and approximate reasoning. It is generated in five main parts: the
input interface, the fuzzification interface, knowledge base unit, decision
making unit and output defuzzification interface. Adaptive neuro-fuzzy
inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic
and neural network. The ANFIS utilizes automatic identification of fuzzy logic
rules and adjustment of membership function (MF). Moreover, neural network can
directly learn from data set to construct fuzzy logic rules and MF implemented
in various applications. FIS settings are evaluated based on two comparisons.
The first evaluation is the comparison between unstructured and structured
events using the same FIS setting. The second comparison is the model settings
between FIS and ANFIS for classifying structured events. The data set consists
of news articles related to terrorism events in three southern provinces of
Thailand. The experimental results show that the classification performance of
the FIS resulting from structured events achieves satisfactory accuracy and is
better than the unstructured events. In addition, the classification of
structured events using ANFIS gives higher performance than the events using
only FIS in the prediction of terrorism events.Comment: IEEE Publication format, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
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
Spectral Pattern Recognition and Fuzzy Artmap Classification: Design Features, System Dynamics and Real World Simulation
Classification of terrain cover from satellite radar imagery represents an area of considerable current interest and research. Most satellite sensors used for land applications are of the imaging type. They record data in a variety of spectral channels and at a variety of ground resolutions. Spectral pattern recognition refers to classification procedures utilizing pixel-by-pixel spectral information as the basis for automated land cover classification. A number of methods have been developed in the past to classify pixels [resolution cells] from multispectral imagery to a priori given land cover categories. Their ability to provide land cover information with high classification accuracies is significant for work where accurate and reliable thematic information is needed. The current trend towards the use of more spectral bands on satellite instruments, such as visible and infrared imaging spectrometers, and finer pixel and grey level resolutions will offer more precise possibilities for accurate identification. But as the complexity of the data grows, so too does the need for more powerful tools to analyse them.
It is the major objective of this study to analyse the capabilities and applicability of the neural pattern recognition system, called fuzzy ARTMAP, to generate high quality classifications of urban land cover using remotely sensed images. Fuzzy ARTMAP synthesizes fuzzy logic and Adaptive Resonance Theory (ART) by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of category choice, search and learning. The paper describes design features, system dynamics and simulation algorithms of this learning system, which is trained and tested for classification (8 a priori given classes) of a multispectral image of a Landsat-5 Thematic Mapper scene (270 x 360 pixels) from the City of Vienna on a pixel-by-pixel basis. Fuzzy ARTMAP performance is compared with that of an error-based learning system based upon the multi-layer perceptron, and the Gaussian maximum likelihood classifier as conventional statistical benchmark on the same database. Both neural classifiers outperform the conventional classifier in terms of classification accuracy. Fuzzy ARTMAP leads to out-of-sample classification accuracies, very close to maximum performance, while the multi-layer perceptron--like the conventional classifier--shows difficulties to distinguish between some land use categories
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