Tree Induction over Perennial Objects

Siddiqui, Zaigham Faraz; Spiliopoulou, Myra

doi:10.1007/978-3-642-13818-8_43

Cited by 6 publications

(10 citation statements)

References 10 publications

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“…The use of sliding windows of a predefined size over each of the streaming relations was initially investigated by Siddiqui and Spiliopoulou [16]. The main assumption that differs from ours is that the streams are synchronized, which means that all the relevant facts will arrive at almost the same time and will be found in the windows of predefined size.…”

Section: A Relational Trees On Streamsmentioning

confidence: 98%

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Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams

Ikonomovska

Driessens

Džeroski

et al. 2011

2011 IEEE 11th International Conference on Data Mining Workshops

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Relational reinforcement learning is a promising branch of reinforcement learning research that deals with structured environments. In these environments, states and actions are differentiated by the presence of certain types of objects and the relations between them and the objects that are involved in the actions. This makes it ultimately suited for tasks that require the manipulation of multiple, interacting objects, such as tasks that a future house-holding robot can be expected to perform like cleaning up a dinner table or storing away done dishes. However, the application of relational reinforcement learning to robotics has been hindered by assumptions such as discrete and atomic state observations. Typical robotic observation systems work in a streaming setup, where objects are discovered and recognized and their placement within their surroundings is determined in a quasi continuous manner instead of a state based one. The resulting information stream can be compared to a set of multiple inter-related data streams. In this paper, we propose an adaptive windowing strategy for generating a stream of learning examples and enabling relational learning from this kind of data. Our approach is independent from the learning algorithm and is based on a gradient search over the space of parameter values, i.e., window sizes, guided by the estimation of the testing error. The proposed algorithm performs online and is data driven and flexible. To the best of our knowledge, this is the first work addressing this problem. Our ideas are empirically supported by an extensive experimental evaluation in a controlled setup using artificial data.

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Section: A Relational Trees On Streamsmentioning

confidence: 98%

“…Thus, most of the existing solutions for relational learning on streams come from the relational database perspective, and mostly for classification and clustering tasks [12]- [16]. Among them, we will focus only on the classification algorithms since we are dealing with supervised learning.…”

Section: A Relational Trees On Streamsmentioning

confidence: 99%

Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams

Ikonomovska

Driessens

Džeroski

et al. 2011

2011 IEEE 11th International Conference on Data Mining Workshops

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“…Nonetheless, new users and new movies may also arrive at any time, hence User and Movie are also streamsstreams of perennial entities. 24 For perennial entities, we use the terms "table" and "stream" interchangeably hereafter, while for a ephemeral records like the ratings we use solely the term "stream". The back-end of our method, xStreams BackEnd, is an adaptive stream mining algorithm that learns a model over the table User -as it is extended with information from the streams Rating and Movie.…”

Section: Learning Task On Multiple Streamsmentioning

confidence: 99%

Learning Relational User Profiles and Recommending Items as Their Preferences Change

Siddiqui¹,

Tiakas

Symeonidis

et al. 2015

Int. J. Artif. Intell. Tools

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Over the last decade a vast number of businesses have developed online e-shops in the web. These online stores are supported by sophisticated systems that manage the products and record the activity of customers. There exist many research works that strive to answer the question "what items are the customers going to like" given their historical profiles. However, most of these works do not take into account the time dimension and cannot respond efficiently when data are huge. In this paper, we study the problem of recommendations in the context of multi-relational stream mining. Our algorithm "xStreams" first separates customers based on their historical data into clusters. It then employs collaborative filtering (CF) to recommend new items to the customers based on their group similarity. To evaluate the working of xStreams, we use a multi-relational data generator for streams. We evaluate xStreams on real and synthetic datasets.

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“…Therefore, we propose to forget old data, not simply by taking old instances out of the sliding window but by eliminating them from the model. Our approach is inspired by the relational stream classifier proposed in [13], which applies an ageing function on the decision tree: if a branch receives no new instances for some time, it is discarded. We build upon this principle by using the age of the instances to decide when to discard them from the model, thereby allowing for arbitrary model learners, not just decision trees.…”

Section: Related Workmentioning

confidence: 99%

“…For backward adaptation, we remove some of the earlier seen documents from the model. Although most stream classifiers use a sliding window over the data, backward adaptation through active elimination of past information from the model is a rather new adaptation modality [13,14,1].…”

Section: Introductionmentioning

confidence: 99%

Adaptive semi supervised opinion classifier with forgetting mechanism

Zimmermann

Ntoutsi

Spiliopoulou

2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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Opinion stream classification methods face the challenge of learning with a limited amount of labeled data: inspecting and labeling opinions is a tedious task, so systems analyzing opinions must devise mechanisms that label the arriving stream of opinionated documents with minimal human intervention. We propose an opinion stream classifier that only uses a seed of labeled documents as input and thereafter adapts itself, as it reads documents with unknown labels. Since the stream of opinions is subject to concept drift, we use two adaptation mechanisms: forward adaptation, where the classifier incorporates to the training set only those unlabeled documents that it considers informative enough in comparison to those seen thus far; and backward adaptation, where the classifier gradually forgets old documents by eliminating them from the model. We evaluate our method on opinionated tweets and show that it performs comparably or even better than a fully supervised baseline.

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Tree Induction over Perennial Objects

Cited by 6 publications

References 10 publications

Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams

Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams

Learning Relational User Profiles and Recommending Items as Their Preferences Change

Adaptive semi supervised opinion classifier with forgetting mechanism

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