Empirical Analysis of Case-Editing Approaches for Numeric Prediction

Redmond, Michael; Highley, Timothy

doi:10.1007/978-90-481-9112-3_14

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…However, the most popular methods have tended to focus on classification [16,[20][21][22][23][24][25], as opposed to regression [17]. Redmond and Highley [17] did try to convert Edited Nearest Neighbors [22] to handle regression by assigning two hyperparameters for agree and accept thresholds, but they acknowledge that applying the classification algorithms to regression is difficult. Our method requires no hyperparameters, though it does require the specification of a prior(s).…”

Section: Related Workmentioning

confidence: 99%

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Kenny

Ruelle

Geoghegan

et al. 2019

Case-Based Reasoning Research and Development

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Smart agriculture has emerged as a rich application domain for AIdriven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture's promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.

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Section: Related Workmentioning

confidence: 99%

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Kenny

Ruelle

Geoghegan

et al. 2019

Case-Based Reasoning Research and Development

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show abstract

“…In these studies no class balancing methodologies were employed. Other approaches such as the fuzzy association rule mining [9] and case-based editing [10] have also been performed.…”

Section: Related Workmentioning

confidence: 99%

Crime Prediction Using Regression and Resources Optimization

Cavadas

Branco

Pereira

2015

Lecture Notes in Computer Science

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Violent crime is a well known social problem affecting both the quality of life and the economical development of a society. Its prediction is therefore an important asset for law enforcement agencies, since due to budget constraints, the optimization of resources is of extreme importance. In this work, we tackle both aspects: prediction and optimization. We propose to predict violent crime using regression and optimize the distribution of police officers through an Integer Linear Programming formulation, taking into account the previous predictions. Although some of the optimization data are synthetic, we propose it as a possible approach for the problem. Experiments showed that Random Forest performs better among the other evaluated learners, after applying the SmoteR algorithm to cope with the rare extreme values. The most severe violent crime rates were predicted for southern states, in accordance with state reports. Accordingly, these were the states with more police officers assigned during optimization.

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Extending greedy feature selection algorithms to multiple solutions

Borboudakis

Tsamardinos

2021

Data Min Knowl Disc

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Most feature selection methods identify only a single solution. This is acceptable for predictive purposes, but is not sufficient for knowledge discovery if multiple solutions exist. We propose a strategy to extend a class of greedy methods to efficiently identify multiple solutions, and show under which conditions it identifies all solutions. We also introduce a taxonomy of features that takes the existence of multiple solutions into account. Furthermore, we explore different definitions of statistical equivalence of solutions, as well as methods for testing equivalence. A novel algorithm for compactly representing and visualizing multiple solutions is also introduced. In experiments we show that (a) the proposed algorithm is significantly more computationally efficient than the TIE* algorithm, the only alternative approach with similar theoretical guarantees, while identifying similar solutions to it, and (b) that the identified solutions have similar predictive performance.

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Empirical Analysis of Case-Editing Approaches for Numeric Prediction

Cited by 4 publications

References 8 publications

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Crime Prediction Using Regression and Resources Optimization

Extending greedy feature selection algorithms to multiple solutions

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