0000000001046992
AUTHOR
Zahra Ahmadi
Prototype-based learning on concept-drifting data streams
Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To ide…
Forest of Normalized Trees: Fast and Accurate Density Estimation of Streaming Data
Density estimation of streaming data is a relevant task in numerous domains. In this paper, a novel non-parametric density estimator called FRONT (forest of normalized trees) is introduced. It uses a structure of multiple normalized trees, segments the feature space of the data stream through a periodically updated linear transformation and is able to adapt to ever evolving data streams. FRONT provides accurate density estimation and performs favorably compared to existing online density estimators in terms of the average log score on multiple standard data sets. Its low complexity, linear runtime as well as constant memory usage, makes FRONT by design suitable for large data streams. Final…
A label compression method for online multi-label classification
Abstract Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally…
Focusing Knowledge-based Graph Argument Mining via Topic Modeling
Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extracting evidence, this paper presents a hybrid model that combines latent Dirichlet allocation and word embeddings to obtain external knowledge from structured and unstructured data. We study the task of sentence-level argument mining, as arguments mostly require some degree of world knowledge to be identified and understood. Given a topic and a sentence, the goal is to classify whether a sentence represents an argument in regard to the topic. We use a topic model to extract topic- and sentence-specific evidence from…
Privacy Preserving Client/Vertical-Servers Classification
We present a novel client/vertical-servers architecture for hybrid multi-party classification problem. The model consists of clients whose attributes are distributed on multiple servers and remain secret during training and testing. Our solution builds privacy-preserving random forests and completes them with a special private set intersection protocol that provides a central commodity server with anonymous conditional statistics. Subsequently, the private set intersection protocol can be used to privately classify the queries of new clients using the commodity server’s statistics. The proviso is that the commodity server must not collude with other parties. In cases where this restriction …
Modeling Multi-label Recurrence in Data Streams
Most of the existing data stream algorithms assume a single label as the target variable. However, in many applications, each observation is assigned to several labels with latent dependencies among them, which their target function may change over time. Classification of such non-stationary multi-label streaming data with the consideration of dependencies among labels and potential drifts is a challenging task. The few existing studies mostly cope with drifts implicitly, and all learn models on the original label space, which requires a lot of time and memory. None of them consider recurrent drifts in multi-label streams and particularly drifts and recurrences visible in a latent label spa…
Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.
Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and w…
Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports
Due to their disclosure required by law, business management reports have become publicly available for a large number of companies, and these reports offer the opportunity to assess the financial health or distress of a company, both quantitatively from the balance sheets and qualitatively from the text. In this paper, we analyze the potential of deep sentiment mining from the textual parts of business management reports and aim to detect signals for financial distress. We (1) created the largest corpus of business reports analyzed qualitatively to date, (2) defined a non-trivial target variable based on the so-called Altman Z-score, (3) developed a filtering of sentences based on class-co…
An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling
The goal of modeling sentences is to accurately represent their meaning for different tasks. A variety of deep learning architectures have been proposed to model sentences, however, little is known about their comparative performance on a common ground, across a variety of datasets, and on the same level of optimization. In this paper, we provide such a novel comparison for two popular architectures, Recursive Neural Tensor Networks (RNTNs) and Convolutional Neural Networks (CNNs). Although RNTNs have been shown to work well in many cases, they require intensive manual labeling due to the vanishing gradient problem. To enable an extensive comparison of the two architectures, this paper empl…