Utilizing Coverage Lists as a Pruning Mechanism for Concept Discovery

Date

2014-10-28

Author

Mutlu, Alev
Doğan, Abdullah
Karagöz, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

117
views

0
downloads

Inductive logic programming (ILP)-based concept discovery systems lack computational efficiency due to the evaluation of the large search spaces they build. One way to tackle this issue is employing pruning mechanisms. In this work, we propose a two-phase pruning mechanism for concept discovery systems that employ an Apriori-like refinement operator and evaluate the goodness of the concept descriptors based on their support value. The first step, which is novel in this work, is computationally inexpensive and prunes the search space based on the coverages of the concept descriptors. The second step employs a widely employed pruning mechanism based on the support value of the concept descriptors. The experimental results show that the first step leaves a search space reduced by 4-22% to be evaluated by the second step, which is more costly.

Subject Keywords

Concept discovery, Pruning, Support, Coverage lists

URI

https://hdl.handle.net/11511/29837

DOI

https://doi.org/10.1007/978-3-319-09465-6_28

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

Suggestions

OpenMETU
Core

Improving Hit Ratio of ILP-based Concept Discovery System with Memoization Mutlu, Alev; Karagöz, Pınar (2014-01-01) Although Inductive Logic Programming (ILP)-based concept discovery systems have applications in a wide range of domains, they still suffer from scalability and efficiency issues. One of the reasons for the efficiency problem is the high number of query executions necessary in the concept discovery process. Owing to the refinement operator of ILP-based concept discovery systems, these queries repeat frequently. In this work, we propose a method to improve the look-up table hit ratio for repeating queries of ...
Interactive visual user interfaces: A survey Murtagh, F; Taşkaya Temizel, Tuğba; Contreras, P; Mothe, J; Englmeier, K (Springer Science and Business Media LLC, 2003-06-01) Following a short survey of input data types on which to construct interactive visual user interfaces, we report on a new and recent implementation taking concept hierarchies as input data. The visual user interfaces express domain ontologies which are based on these concept hierarchies. We detail a web-based implementation, and show examples of usage. An appendix surveys related systems, many of them commercial.
An adaptive, energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensor networks Deniz, Fatih; Bagci, Hakki; KÖRPEOĞLU, İBRAHİM; Yazıcı, Adnan (2016-07-01) This paper introduces an adaptive, energy-aware and distributed fault-tolerant topology control algorithm, namely the Adaptive Disjoint Path Vector (ADPV) algorithm, for heterogeneous wireless sensor networks. In this heterogeneous model, we have resource-rich supernodes as well as ordinary sensor nodes that are supposed to be connected to the supernodes. Unlike the static alternative Disjoint Path Vector (DPV) algorithm, the focus of ADPV is to secure supernode connectivity in the presence of node failures...
A temporal neural network model for constructing connectionist expert system knowledge bases Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01) This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
Representing temporal knowledge in connectionist expert systems Alpaslan, Ferda Nur (1996-09-27) This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...

Citation Formats

A. Mutlu, A. Doğan, and P. Karagöz, “Utilizing Coverage Lists as a Pruning Mechanism for Concept Discovery,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/29837.