Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification

Aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them focus on categorizing an image into one semantic label, while in the real world, an aerial image is often associated with multiple labels, e.g., multiple object-level labels in our case. Besides, a comprehensive picture of present objects in a given high resolution aerial image can provide more in-depth understanding of the studied region. For these reasons, aerial image multi-label classification has been attracting increasing attention. However, one common limitation shared by existing methods in the community is that the co-occurrence relationship of various classes, so called class dependency, is underexplored and leads to an inconsiderate decision. In this paper, we propose a novel end-to-end network, namely class-wise attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM), for this task. The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network. Particularly, the feature extraction module is designed for extracting fine-grained semantic feature maps, while the class attention learning layer aims at capturing discriminative class-specific features. As the most important part, the bidirectional LSTM-based sub-network models the underlying class dependency in both directions and produce structured multiple object labels. Experimental results on UCM multi-label dataset and DFC15 multi-label dataset validate the effectiveness of our model quantitatively and qualitatively

Multi-task learning for intelligent data processing in granular computing context

Classification is a popular task in many application areas, such as decision making, rating, sentiment analysis and pattern recognition. In the recent years, due to the vast and rapid increase in the size of data, classification has been mainly undertaken in the way of supervised machine learning. In this context, a classification task involves data labelling, feature extraction,feature selection and learning of classifiers. In traditional machine learning, data is usually single-labelled by experts, i.e., each instance is only assigned one class label, since experts assume that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in real applications. For example, in the context of emotion detection, there could be more than one emotion identified from the same person. On the other hand, feature selection has typically been done by evaluating feature subsets in terms of their relevance to all the classes. However, it is possible that a feature is only relevant to one class, but is irrelevant to all the other classes. Based on the above argumentation on data labelling and feature selection, we propose in this paper a framework of multi-task learning. In particular, we consider traditional machine learning to be single task learning, and argue the necessity to turn it into multi-task learning to allow an instance to belong to more than one class (i.e., multi-task classification) and to achieve class specific feature selection (i.e.,multi-task feature selection). Moreover, we report two experimental studies in terms of fuzzy multi-task classification and rule learning based multi-task feature selection. The results show empirically that it is necessary to undertake multi-task learning for both classification and feature selection

Multi-Model Network Intrusion Detection System Using Distributed Feature Extraction and Supervised Learning

Intrusion Detection Systems (IDSs) monitor network traffic and system activities to identify any unauthorized or malicious behaviors. These systems usually leverage the principles of data science and machine learning to detect any deviations from normalcy by learning from the data associated with normal and abnormal patterns. The IDSs continue to suffer from issues like distributed high-dimensional data, inadequate robustness, slow detection, and high false-positive rates (FPRs). We investigate these challenges, determine suitable strategies, and propose relevant solutions based on the appropriate mathematical and computational concepts. To handle high-dimensional data in a distributed network, we optimize the feature space in a distributed manner using the PCA-based feature extraction method. The experimental results display that the classifiers built upon the features so extracted perform well by giving a similar level of accuracy as given by the ones that use the centrally extracted features. This method also significantly reduces the cumulative time needed for extraction. By utilizing the extracted features, we construct a distributed probabilistic classifier based on Naïve Bayes. Each node counts the local frequencies and passes those on to the central coordinator. The central coordinator accumulates the local frequencies and computes the global frequencies, which are used by the nodes to compute the required prior probabilities to perform classifications. Each node, being evenly trained, is capable of detecting intrusions individually to improve the overall robustness of the system. We also propose a similarity measure-based classification (SMC) technique that works by computing the cosine similarities between the class-specific frequential weights of the values in an observed instance and the average frequency-based data centroid. An instance is classified into the class whose weights for the values in it share the highest level of similarity with the centroid. SMC contributes alongside Naïve Bayes in a multi-model classification approach, which we introduce to reduce the FPR and improve the detection accuracy. This approach utilizes the similarities associated with each class label determined by SMC and the probabilities associated with each class label determined by Naïve Bayes. The similarities and probabilities are aggregated, separately, to form new features that are used to train and validate a tertiary classifier. We demonstrate that such a multi-model approach can attain a higher level of accuracy compared with the single-model classification techniques. The contributions made by this dissertation to enhance the scalability, robustness, and accuracy can help improve the efficacy of IDSs

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.Comment: 12 pages for EMNLP 201