Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images

We propose a novel scheme for designing fuzzy rule based classifier. An SOFM based method is used for generating a set of prototypes which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different context leading to context sensitive inferencing. To realize context sensitive inferencing we used a softmin operator with a tunable parameter. The proposed scheme is tested on several multispectral satellite image data sets and the performance is found to be much better than the results reported in the literature.Comment: 23 pages, 7 figure

Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a possibilistic label vector for each pixel using the fuzzy rule base. To exploit the spatial correlation of land cover types we propose four different information aggregation methods which use the possibilistic class label of a pixel and those of its eight spatial neighbors for making the final classification decision. Three of the aggregation methods use Dempster-Shafer theory of evidence while the remaining one is modeled after the fuzzy k-NN rule. The proposed methods are tested with two benchmark seven channel satellite images and the results are found to be quite satisfactory. They are also compared with a Markov random field (MRF) model-based contextual classification method and found to perform consistently better.Comment: 14 pages, 2 figure

A fuzzy approach to text classification with two-stage training for ambiguous instances

Sentiment analysis is a very popular application area of text mining and machine learning. The popular methods include Support Vector Machine, Naive Bayes, Decision Trees and Deep Neural Networks. However, these methods generally belong to discriminative learning, which aims to distinguish one class from others with a clear-cut outcome, under the presence of ground truth. In the context of text classification, instances are naturally fuzzy (can be multi-labeled in some application areas) and thus are not considered clear-cut, especially given the fact that labels assigned to sentiment in text represent an agreed level of subjective opinion for multiple human annotators rather than indisputable ground truth. This has motivated researchers to develop fuzzy methods, which typically train classifiers through generative learning, i.e. a fuzzy classifier is used to measure the degree to which an instance belongs to each class. Traditional fuzzy methods typically involve generation of a single fuzzy classifier and employ a fixed rule of defuzzification outputting the class with the maximum membership degree. The use of a single fuzzy classifier with the above fixed rule of defuzzification is likely to get the classifier encountering the text ambiguity situation on sentiment data, i.e. an instance may obtain equal membership degrees to both the positive and negative classes. In this paper, we focus on cyberhate classification, since the spread of hate speech via social media can have disruptive impacts on social cohesion and lead to regional and community tensions. Automatic detection of cyberhate has thus become a priority research area. In particular, we propose a modified fuzzy approach with two stage training for dealing with text ambiguity and classifying four types of hate speech, namely: religion, race, disability and sexual orientation - and compare its performance with those popular methods as well as some existing fuzzy approaches, while the features are prepared through the Bag-of-Words and Word Embedding feature extraction methods alongside the correlation based feature subset selection method. The experimental results show that the proposed fuzzy method outperforms the other methods in most cases

SPOCC: Scalable POssibilistic Classifier Combination -- toward robust aggregation of classifiers

We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions in the possibility theory framework. For each classifier prediction, we build a possibility distribution assessing how likely the classifier prediction is correct using frequentist probabilities estimated on the validation set. The possibility distributions are aggregated using an adaptive t-norm that can accommodate dependency and poor accuracy of the classifier predictions. We prove that the proposed approach possesses a number of desirable classifier combination robustness properties

Remote Sensing and Data Fusion for Eucalyptus Trees Identification

Satellite remote sensing is supported by the extraction of data/information from satellite images or aircraft, through multispectral images, that allows their remote analysis and classification. Analyzing those images with data fusion tools and techniques, seem a suitable approach for the identification and classification of land cover. This land cover classification is possible because the fusion/merging techniques can aggregate various sources of heterogeneous information to generate value-added products that facilitate features classification and analysis. This work proposes to apply a data fusion algorithm, denoted FIF (Fuzzy Information Fusion), which combines computational intelligence techniques with multicriteria concepts and techniques to automatically distinguish Eucalyptus trees, in satellite images To assess the proposed approach, a Portuguese region, which includes planted Eucalyptus, will be used. This region is chosen because it includes a significant number of eucalyptus, and, currently, it is hard to automatically distinguish them from other types of trees (through satellite images), which turns this study into an interesting experiment of using data fusion techniques to differentiate types of trees. Further, the proposed approach is tested and validated with several fusion/aggregation operators to verify its versatility. Overall, the results of the study demonstrate the potential of this approach for automatic classification of land types.A deteção remota de imagens de satélite é baseada na extração de dados / informações de imagens de satélite ou aeronaves, através de imagens multiespectrais, que permitem a sua análise e classificação. Quando estas imagens são analisadas com ferramentas e técnicas de fusão de dados, torna-se num método muito útil para a identificação e classificação de diferentes tipos de ocupação de solo. Esta classificação é possível porque as técnicas de fusão podem processar várias fontes de informações heterogéneas, procedendo depois à sua agregação, para gerar produtos de valor agregado que facilitam a classificação e análise de diferentes entidades - neste caso a deteção de eucaliptos. Esta dissertação propõe a utilização de um algoritmo, denominado FIF (Fuzzy Information Fusion), que combina técnicas de inteligência computacional com conceitos e técnicas multicritério. Para avaliar o trabalho proposto, será utilizada uma região portuguesa, que inclui uma vasta área de eucaliptos. Esta região foi escolhida porque inclui um número significativo de eucaliptos e, atualmente, é difícil diferenciá-los automaticamente de outros tipos de árvores (através de imagens de satélite), o que torna este estudo numa experiência interessante relativamente ao uso de técnicas de fusão de dados para diferenciar tipos de árvores. Além disso, o trabalho desenvolvido será testado com vários operadores de fusão/agregação para verificar sua versatilidade. No geral, os resultados do estudo demonstram o potencial desta abordagem para a classificação automática de diversos tipos de ocupação de solo (e.g. água, árvores, estradas etc)

Evidential combination of pedestrian detectors

International audienceThe importance of pedestrian detection in many applications has led to the development of many algorithms. In this paper, we address the problem of combining the outputs of several detectors. A pre-trained pedestrian detector is seen as a black box returning a set of bounding boxes with associated scores. A calibration step is first conducted to transform those scores into a probability measure. The bounding boxes are then grouped into clusters and their scores are combined. Different combination strategies using the theory of belief functions are proposed and compared to probabilistic ones. A combination rule based on triangular norms is used to deal with dependencies among detectors. More than 30 state-of-the-art detectors were combined and tested on the Caltech Pedestrian Detection Benchmark. The best combination strategy outperforms the currently best performing detector by 9% in terms of log-average miss rate