| 1. |
- Beyene, Ayne A., et al.
(författare)
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Improved concept drift handling in surgery prediction and other applications
- 2015
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Ingår i: Knowledge and Information Systems. - : Springer. - 0219-1377 .- 0219-3116. ; 44:1, s. 177-196
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Tidskriftsartikel (refereegranskat)abstract
- The article presents a new algorithm for handling concept drift: the Trigger-based Ensemble (TBE) is designed to handle concept drift in surgery prediction but it is shown to perform well for other classification problems as well. At the primary care, queries about the need for surgical treatment are referred to a surgeon specialist. At the secondary care, referrals are reviewed by a team of specialists. The possible outcomes of this review are that the referral: (i) is canceled, (ii) needs to be complemented, or (iii) is predicted to lead to surgery. In the third case, the referred patient is scheduled for an appointment with a surgeon specialist. This article focuses on the binary prediction of case three (surgery prediction). The guidelines for the referral and the review of the referral are changed due to, e.g., scientific developments and clinical practices. Existing decision support is based on the expert systems approach, which usually requires manual updates when changes in clinical practice occur. In order to automatically revise decision rules, the occurrence of concept drift (CD) must be detected and handled. The existing CD handling techniques are often specialized; it is challenging to develop a more generic technique that performs well regardless of CD type. Experiments are conducted to measure the impact of CD on prediction performance and to reduce CD impact. The experiments evaluate and compare TBE to three existing CD handling methods (AWE, Active Classifier, and Learn++) on one real-world dataset and one artificial dataset. TBA significantly outperforms the other algorithms on both datasets but is less accurate on noisy synthetic variations of the real-world dataset.
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| 2. |
- Boldt, Martin, et al.
(författare)
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Anomaly detection of event sequences using multiple temporal resolutions and Markov chains
- 2020
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Ingår i: Knowledge and Information Systems. - : Springer London. - 0219-1377 .- 0219-3116. ; 62, s. 669-686
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Tidskriftsartikel (refereegranskat)abstract
- Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, F 1 -score and Jaccard index when compared to k-means clustering of the sessions. © 2019, The Author(s).
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| 3. |
- Casas-Roma, Jordi, et al.
(författare)
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k-Degree anonymity and edge selection : improving data utility in large networks
- 2017
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Ingår i: Knowledge and Information Systems. - : Springer. - 0219-1377 .- 0219-3116. ; 50:2, s. 447-474
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Tidskriftsartikel (refereegranskat)abstract
- The problem of anonymization in large networks and the utility of released data are considered in this paper. Although there are some anonymization methods for networks, most of them cannot be applied in large networks because of their complexity. In this paper, we devise a simple and efficient algorithm for k-degree anonymity in large networks. Our algorithm constructs a k-degree anonymous network by the minimum number of edge modifications. We compare our algorithm with other well-known k-degree anonymous algorithms and demonstrate that information loss in real networks is lowered. Moreover, we consider the edge relevance in order to improve the data utility on anonymized networks. By considering the neighbourhood centrality score of each edge, we preserve the most important edges of the network, reducing the information loss and increasing the data utility. An evaluation of clustering processes is performed on our algorithm, proving that edge neighbourhood centrality increases data utility. Lastly, we apply our algorithm to different large real datasets and demonstrate their efficiency and practical utility.
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| 4. |
- Ciaperoni, Martino, et al.
(författare)
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Concise and interpretable multi-label rule sets
- 2023
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Ingår i: Knowledge and Information Systems. - : Springer Nature. - 0219-1377 .- 0219-3116. ; 65:12, s. 5657-5694
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Tidskriftsartikel (refereegranskat)abstract
- Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as a concise set of simple “if-then” rules, and thus, it offers better interpretability compared to black-box models. Notably, our method is able to find a small set of relevant patterns that lead to accurate multi-label classification, while existing rule-based classifiers are myopic and wasteful in searching rules, requiring a large number of rules to achieve high accuracy. In particular, we formulate the problem of choosing multi-label rules to maximize a target function, which considers not only discrimination ability with respect to labels, but also diversity. Accounting for diversity helps to avoid redundancy, and thus, to control the number of rules in the solution set. To tackle the said maximization problem, we propose a 2-approximation algorithm, which circumvents the exponential-size search space of rules using a novel technique to sample highly discriminative and diverse rules. In addition to our theoretical analysis, we provide a thorough experimental evaluation and a case study, which indicate that our approach offers a trade-off between predictive performance and interpretability that is unmatched in previous work.
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| 5. |
- Drabent, Wlodzimierz, et al.
(författare)
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Hybrid Rules with Well-Founded Semantics
- 2010
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Ingår i: Knowledge and Information Systems. - Berlin : Springer. - 0219-1377 .- 0219-3116. ; 25:1, s. 137-168
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Tidskriftsartikel (refereegranskat)abstract
- A general framework is proposed for integration of rules and external first-order theories. It is based on the well-founded semantics of normal logic programs and inspired by ideas of Constraint Logic Programming (CLP) and constructive negation for logic programs. Hybrid rules are normal clauses extended with constraints in the bodies; constraints are certain formulae in the language of the external theory. A hybrid program consists of a set of hybrid rules and an external theory. Instances of the framework are obtained by specifying the class of external theories and the class of constraints. An example instance is integration of (non-disjunctive) Datalog with ontologies formalized in description logics. The paper defines a declarative semantics of hybrid programs and a goal-driven formal operational semantics. The latter can be seen as a generalization of SLS-resolution. It provides a basis for hybrid implementations combining Prolog with constraint solvers (such as ontology reasoners). Soundness of the operational semantics is proven. Sufficient conditions for decidability of the declarative semantics and for completeness of the operational semantics are given.
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| 6. |
- Duma, Ramadhani Ally, et al.
(författare)
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Fake review detection techniques, issues, and future research directions: a literature review
- 2024
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Ingår i: Knowledge and Information Systems. - : Springer Nature. - 0219-1377 .- 0219-3116.
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Forskningsöversikt (refereegranskat)abstract
- Recently, the impact of product or service reviews on customers' purchasing decisions has become increasingly significant in online businesses. Consequently, manipulating reviews for fame or profit has become prevalent, with some businesses resorting to paying fake reviewers to post spam reviews. Given the importance of reviews in decision-making, detecting fake reviews is crucial to ensure fair competition and sustainable e-business practices. Although significant efforts have been made in the last decade to distinguish credible reviews from fake ones, it remains challenging. Our literature review has identified several gaps in the existing research: (1) most fake review detection techniques have been proposed for high-resource languages such as English and Chinese, and few studies have investigated low-resource and multilingual fake review detection, (2) there is a lack of research on deceptive review detection for reviews based on language code-switching (code-mix), (3) current multi-feature integration techniques extract review representations independently, ignoring correlations between them, and (4) there is a lack of a consolidated model that can mutually learn from review emotion, coarse-grained (overall rating), and fine-grained (aspect ratings) features to supplement the problem of sentiment and overall rating inconsistency. In light of these gaps, this study aims to provide an in-depth literature analysis describing strengths and weaknesses, open issues, and future research directions.
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| 7. |
- Fakas, Georgios, et al.
(författare)
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Electronic roads : intelligent navigation through multi-contextual information
- 2004
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Ingår i: Knowledge and Information Systems. - : Springer-Verlag. - 0219-1377 .- 0219-3116. ; 6:1, s. 103-124
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a model for intelligent navigation through multi-contextual information that could form electronic roads in the information society. This paper aims to address the problem of electronic information roads, define their notion and the technical form they can take as well as present the tools developed for implementing such a system. The main objective of the proposed model is to give the traveler the capability of exploring the information space in a natural way where the information offered will remain continuously interesting. The system offers links to information in a dynamic and adaptive way. This is achieved by employing intelligent navigation techniques, which combine user profiling and meta-data. Electronic roads emphasize the presentation of multi-contextual information, i.e., information that is semantically related but of different nature at different locations and time. An electronic road is the user’s navigation path through a series of information units. Information units are the building blocks of the available cultural information content.
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| 8. |
- Guerrero, Esteban, et al.
(författare)
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Activity qualifiers using an argument-based construction
- 2018
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Ingår i: Knowledge and Information Systems. - : Springer. - 0219-1377 .- 0219-3116. ; 54:3, s. 633-658
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Tidskriftsartikel (refereegranskat)abstract
- Based on an argumentation theory approach, we present a novel method for evaluating complex goal-based activities by generalizing a notion of qualifier defined in the health domain. Three instances of the general qualifier are proposed: Performance, Actuation and Capacity; the first one evaluates what a person does, the second how an individual follows an action plan, and the third one how "well" or "bad" an activity is executed. Qualifiers are intended to be used by autonomous systems for evaluating human activity. We exemplify our approach using a health domain assessment protocol. Main results of this test show a partial correlation between ambiguities assessed by experts and our argument-based approach; and a multi-dimensional perspective how an activity is executed when a combined evaluation of qualifiers is used. This last outcome was interesting for some therapists consulted. Results also show differences between values of qualifiers using different argumentation semantics; two scenarios were proposed by therapist for using different semantics: preliminary activity screening and time-span follow-up evaluation.
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| 9. |
- Görnerup, Olof, et al.
(författare)
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Domain-agnostic discovery of similarities and concepts at scale
- 2017
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Ingår i: Knowledge and Information Systems. - London : Springer. - 0219-1377 .- 0219-3116. ; 51:2, s. 531-560
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Tidskriftsartikel (refereegranskat)abstract
- Appropriately defining and efficiently calculating similarities from large data sets are often essential in data mining, both for gaining understanding of data and generating processes and for building tractable representations. Given a set of objects and their correlations, we here rely on the premise that each object is characterized by its context, i.e., its correlations to the other objects. The similarity between two objects can then be expressed in terms of the similarity between their contexts. In this way, similarity pertains to the general notion that objects are similar if they are exchangeable in the data. We propose a scalable approach for calculating all relevant similarities among objects by relating them in a correlation graph that is transformed to a similarity graph. These graphs can express rich structural properties among objects. Specifically, we show that concepts—abstractions of objects—are constituted by groups of similar objects that can be discovered by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of fields and will be demonstrated here in three domains: computational linguistics, music, and molecular biology, where the numbers of objects and correlations range from small to very large.
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| 10. |
- Karlsson, Isak, et al.
(författare)
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Locally and globally explainable time series tweaking
- 2020
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Ingår i: Knowledge and Information Systems. - : Springer Science and Business Media LLC. - 0219-1377 .- 0219-3116. ; 62:5, s. 1671-1700
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Tidskriftsartikel (refereegranskat)abstract
- Time series classification has received great attention over the past decade with a wide range of methods focusing on predictive performance by exploiting various types of temporal features. Nonetheless, little emphasis has been placed on interpretability and explainability. In this paper, we formulate the novel problem of explainable time series tweaking, where, given a time series and an opaque classifier that provides a particular classification decision for the time series, we want to find the changes to be performed to the given time series so that the classifier changes its decision to another class. We show that the problem is NP -hard, and focus on three instantiations of the problem using global and local transformations. In the former case, we investigate the k-nearest neighbor classifier and provide an algorithmic solution to the global time series tweaking problem. In the latter case, we investigate the random shapelet forest classifier and focus on two instantiations of the local time series tweaking problem, which we refer to as reversible and irreversible time series tweaking, and propose two algorithmic solutions for the two problems along with simple optimizations. An extensive experimental evaluation on a variety of real datasets demonstrates the usefulness and effectiveness of our problem formulation and solutions.
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