On Dataset Complexity for Case Base Maintenance

Cummins, Lisa; Bridge, Derek

doi:10.1007/978-3-642-23291-6_6

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…F1 uses the largest discriminating ratio among all the dimensions provided by the encoding method, indicating if the problem classes can be separable using this high-discriminant feature. F2 is related to the overlapping intervals between the problem classes [10]. The Average Number of Principal Component Analysis (PCA) dimensions compared to the original dimensions (T4) can be used for evaluating dimensionality [20].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Trace Encoding Methods in Process Mining

Barbon

Ceravolo

Damiani

et al. 2021

Lecture Notes in Computer Science

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Encoding methods affect the performance of process mining tasks but little work in the literature focused on quantifying their impact. In this paper, we compare 10 different encoding methods from three different families (trace replay and alignment, graph embeddings, and word embeddings) using measures to evaluate the overlaps in the feature space, the accuracy obtained, and the computational resources (time) consumed with a classification task. Across hundreds of event logs representing four variations of five scenarios and five anomalies, it was possible to identify the edge2vec method as the most accurate and effective in reducing class overlapping in the feature space.

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Section: Introductionmentioning

confidence: 99%

Evaluating Trace Encoding Methods in Process Mining

Barbon

Ceravolo

Damiani

et al. 2021

Lecture Notes in Computer Science

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“…Before the instance level was addressed, complexity measures were applied in tasks like IS [18,11] or to assess whether it is worthwhile to apply a noise filter to the instances or not [26]. However, the instance perspective of data complexity has fostered their use in tasks related to IS like noise filter or data sampling.…”

Section: State-of-the-artmentioning

confidence: 99%

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Aceña,

Lancho,

de Diego

et al. 2024

Preprint

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Within the scope of Machine Learning (ML), Instance Selection (IS) is a sampling process that consists of filtering noise and removing redundant data. In classification problems, IS implies a compromise between maximizing performance and reducing the sample size used for training. Complexity measures provide relevant information about the difficulty of classifying instances, which makes them appropriate for IS since they can capture, for example, noisy or borderline points. This paper introduces the complexity measure DDN, defined at three levels: instance, class, and dataset. The Dynamic Disagreeing Neighbors (DDN) of an instance is defined as the percentage of its nearest neighbors that belong to other classes. The DDN is based on the Nearest Centroid Neighbors (NCN) neighborhood computation, which is dynamically adjusted to the data distribution. In addition, the distance of each neighbor is taken into account so that those farther away are less influential and those closer are more influential for the instance complexity. The validity of the proposal is evaluated through a series of experiments where it is compared with the widely known k-Disagreeing Neighbors (kDN) in terms of stability, correlation with classification error, and performance in IS. The DDN has shown competitive, stable, and robust results throughout the experiments, generally improving on those obtained with the alternative.

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“…(3) Reachability(a ∈ A) = {a ′ ∈ A : solves(a ′ , a)}, with the unedited original training data [15]. CNN is further extended as reduced edited nearest neighbor method in [8]. The noisy cases are removed, which belong to a class different than the majority of their NN's [8].…”

Section: Related Workmentioning

confidence: 99%

“…CNN is further extended as reduced edited nearest neighbor method in [8]. The noisy cases are removed, which belong to a class different than the majority of their NN's [8].…”

Section: Related Workmentioning

confidence: 99%

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Khan

Hayat

Awan

2019

Hum. Cent. Comput. Inf. Sci.

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Case-based reasoning (CBR) is one of the widely used lazy machine learning methods inspired by natural learning behavior towards solving a new problem [1, 9, 22, 50]. In lazy machine learning methods, training of the model evolves with time and presentation of more data. As the new data is presented, it continues contributing in improvement of the learning curve of the system. CBR is implemented as a learning layer onto the problem domain. Each new problem instance which needs to be resolved through this learning method is presented as a case. It is matched with the cases present in the knowledge repository called case-base. Set of the nearest neighbors of a new problem is extracted using a selected similarity measure. Solutions of the retrieved nearest neighbors of the case at hand are combined together appropriately to figure out the new solution. It may undergo a fine tuning exercise if the need arises. Finally, the new case comprising of the problem and its relative solution is added into the existing Abstract Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study.

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On Dataset Complexity for Case Base Maintenance

Cited by 11 publications

References 11 publications

Evaluating Trace Encoding Methods in Process Mining

Evaluating Trace Encoding Methods in Process Mining

Dynamic Disagreeing Neighbors: A complexity measure for instance selection

Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

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