Enhancing K-Means using class labels

Peralta, Billy; Espinace, Pablo; Soto, Álvaro

doi:10.3233/ida-130618

Cited by 8 publications

(3 citation statements)

References 26 publications

(19 reference statements)

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“…All the collected ERPs are first divided into four groups according to the category of the corresponding emergencies, i.e., natural disasters, accident disasters, public health incidents, and social security incidents. Then, we apply an enhanced k-Means clustering algorithm [27] to each group of ERPs to derive a more fine-grained category of ERPs. Finally, 33 clusters are obtained, each taken as the category label of ERPs.…”

Section: ) Web Crawlingmentioning

confidence: 99%

Rapid Generation of Emergency Response Plans for Unconventional Emergencies

Liu

Chen

2020

IEEE Access

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Unconventional emergencies can lead to unforeseen disastrous penalties. Due to their unrepeatable, complex, and unpredictable nature, it is generally hard to establish high-quality Emergency Response Plans (ERPs) for unconventional emergencies, thus posing great challenges for unconventional emergency response. This work proposes a rapid ERP generation approach for unconventional emergencies so as to provide support for emergency decision-making. The generation of ERPs is achieved by exploitation of existing ERPs that contain much emergency response experience. First, a number of ERPs are collected and structurally organized to construct an ERP repository. Then, the applicability of each ERP segment in the repository to a given unconventional emergency is evaluated by a proposed ERP similarity measure and emergency scenario matching mechanism, in which the semantic relevance and the scenario consistency of an ERP segment are taken into account, respectively. Applicable ERP segments are obtained for each section of ERPs, and combined to form a new ERP with the guidance of pre-defined ERP structure. Furthermore, we design an ERP assessment method and perform a case study on the proposed approach which presents encouraging experimental results.

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Section: ) Web Crawlingmentioning

confidence: 99%

Rapid Generation of Emergency Response Plans for Unconventional Emergencies

Liu

Chen

2020

IEEE Access

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“…These techniques (SSSKMIV, SSSKMCSQ, SSSKMIV SSE ) have some similarities with the k-means semi-supervised segmentation algorithm, proposed in Peralta et al 19 which is called LK-Means. This methodology has many similarities to SSS techniques, but also has a number of clear differences.…”

Section: Variationmentioning

confidence: 99%

empirical investigation of alternative semi-supervised segmentation methodologies

Breed¹,

Verster²

2019

S. Afr. J. Sci.

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Segmentation of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main types of segmentation and examples of improved performance of predictive models exist for both approaches. However, both focus on a single aspect – either target separation or independent variable distribution – and combining them may deliver better results. This combination approach is called semi-supervised segmentation. Our objective was to explore four new semi-supervised segmentation techniques that may offer alternative strengths. We applied these techniques to six data sets from different domains, and compared the model performance achieved. The original semi-supervised segmentation technique was the best for two of the data sets (as measured by the improvement in validation set Gini), but others outperformed for the other four data sets. Significance: We propose four newly developed semi-supervised segmentation techniques that can be used as additional tools for segmenting data before fitting a logistic regression. In all comparisons, using semi-supervised segmentation before fitting a logistic regression improved the modelling performance (as measured by the Gini coefficient on the validation data set) compared to using unsegmented logistic regression.

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“…Other efforts do not estimate probability densities at all, such as "supervised clustering" (Eick et al, 2004). Many applications of this paradigm exist (Finley and Joachims, 2005;Al-Harbi and Rayward-Smith, 2006;DiCicco and Patel, 2010;Peralta et al, 2013;Ramani and Jacob, 2013;Grbovic et al, 2013;Peralta et al, 2016;Flammarion et al, 2016;Ismaili et al, 2016;Yoon et al, 2016;Dhurandhar et al, 2017).…”

Section: Case Study: Prediction-constrained Mixture Modelsmentioning

confidence: 99%

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models

Hughes¹,

Weiner²,

Hope³

et al. 2017

Preprint

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Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a framework for training latent variable models that explicitly balances two goals: recovery of faithful generative explanations of high-dimensional data, and accurate prediction of associated semantic labels. Existing approaches fail to achieve these goals due to an incomplete treatment of a fundamental asymmetry: the intended application is always predicting labels from data, not data from labels. Our prediction-constrained objective for training generative models coherently integrates loss-based supervisory signals while enabling effective semisupervised learning from partially labeled data. We derive learning algorithms for semi-supervised mixture and topic models using stochastic gradient descent with automatic differentiation. We demonstrate improved prediction quality compared to several previous supervised topic models, achieving predictions competitive with high-dimensional logistic regression on text sentiment analysis and electronic health records tasks while simultaneously learning interpretable topics.

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Enhancing K-Means using class labels

Cited by 8 publications

References 26 publications

Rapid Generation of Emergency Response Plans for Unconventional Emergencies

Rapid Generation of Emergency Response Plans for Unconventional Emergencies

empirical investigation of alternative semi-supervised segmentation methodologies

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models

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