An algorithm for a selective nearest neighbor decision rule (Corresp.)

Ritter, G. L.; Woodruff, H. B.; Lowry, S. R.; Isenhour, T. L.

doi:10.1109/tit.1975.1055464

Cited by 300 publications

(153 citation statements)

References 8 publications

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“…This is the case of Condensed Nearest Neighbor (CNN) [104], one of the oldest, the SNN (Selective Nearest Neighbor rule) method [197], and the Generalized Condensed Nearest Neighbor rule (GCNN) [44]. Other early instance selection methods focused on discarding noisy instances in the training set, like the Edited Nearest Neighbor method [226] and some variations like the all k-NN method [215] and the Multiedit method [55].…”

Section: Wrapper Instance Selectionmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.

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Section: Wrapper Instance Selectionmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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“…This technique is very sensitive to noise and to the order of presentation of the training set cases. Ritter [10] reported improvements on the CNN with his Selective Nearest Neighbour (SNN) which imposes the rule that every case in the training set must be closer to a case of the same class in the edited set than to any other training case of a different class. Gates [11] introduced a decremental technique which starts with the edited set equal to the training set and removes a case from the edited set where its removal does not cause any other training case to be misclassified.…”

Section: Early Techniquesmentioning

confidence: 99%

An Analysis of Case-Base Editing in a Spam Filtering System

Delany¹,

Cunningham

2004

Lecture Notes in Computer Science

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Abstract.Because of the volume of spam email and its evolving nature, any deployed Machine Learning-based spam filtering system will need to have procedures for case-base maintenance. Key to this will be procedures to edit the case-base to remove noise and eliminate redundancy. In this paper we present a two stage process to do this. We present a new noise reduction algorithm called Blame-Based Noise Reduction that removes cases that are observed to cause misclassification. We also present an algorithm called Conservative Redundancy Reduction that is much less aggressive than the state-of-the-art alternatives and has significantly better generalisation performance in this domain. These new techniques are evaluated against the alternatives in the literature on four datasets of 1000 emails each (50% spam and 50% non spam).

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“…As will be shown below CNN condenses on the average the number of vectors three times. The performance of CNN algorithm is not good, but this model inspired construction of new methods such as SNN by Ritter et al [5], RNN by Gates [6] or ENN by Wilson [7]. A group of three algorithms were inspired by encoding length principle [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Instances Seletion Algorithms I. Algorithms Survey

Jankowski

Grochowski

2004

Lecture Notes in Computer Science

109

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Abstract. Several methods were proposed to reduce the number of instances (vectors) in the learning set. Some of them extract only bad vectors while others try to remove as many instances as possible without significant degradation of the reduced dataset for learning. Several strategies to shrink training sets are compared here using different neural and machine learning classification algorithms. In part II (the accompanying paper) results on benchmarks databases have been presented.

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An algorithm for a selective nearest neighbor decision rule (Corresp.)

Cited by 300 publications

References 8 publications

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

An Analysis of Case-Base Editing in a Spam Filtering System

Comparison of Instances Seletion Algorithms I. Algorithms Survey

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