Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Calvo-Zaragoza, Jorge; Valero-Mas, Jose J.; Rico-Juan, Juan Ramón

doi:10.1016/j.patcog.2014.11.015

Cited by 63 publications

(29 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since trying to maintain the same accuracy as with the initial training set is difficult to fulfill in practical scenarios, much research has been recently devoted to enhance this process through the combination with other techniques. Some of these include Feature Selection [35], Ensemble methods [20] or modifications to the kNN rule [7].…”

Section: Background On Data Reductionmentioning

confidence: 99%

Prototype generation on structural data using dissimilarity space representation

Calvo-Zaragoza

Valero-Mas

Rico-Juan

2016

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

Data Reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, Prototype Selection (PS) and Prototype Generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. This work studies the possibility of using Dissimilarity Space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.

show abstract

Section: Background On Data Reductionmentioning

confidence: 99%

Prototype generation on structural data using dissimilarity space representation

Calvo-Zaragoza

Valero-Mas

Rico-Juan

2016

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…The k-Nearest Neighbors classifier frequently used in Case-based reasoning remains as (i) one of the most well-known algorithms for supervised non-parametric classification (Duda et al, 2001) in Pattern Recognition, data mining and Case based maintenance (Calvo-Zaragoza et al, 2014;Ferrandiz and Boullé, 2010) (ii) as a benchmark for experimental studies in machine learning (Leyva et al 2015).…”

Section: Case-based Maintenancementioning

confidence: 99%

Case-based maintenance: Structuring and incrementing the case base

Chebel-Morello

Haouchine

Zerhouni

2015

Knowledge-Based Systems

View full text Add to dashboard Cite

Please cite this article as: B. Chebel-Morello, M.K. Haouchine, N. Zerhouni, Case-based Maintenance: Structuring and incrementing the Case Base, Knowledge-Based Systems (2015), doi: http://dx. Abstract.To avoid performance degradation and maintain the quality of results obtained by the case-based reasoning (CBR) systems, maintenance becomes necessary, especially for those systems designed to operate over long periods and which must handle large numbers of cases. CBR systems cannot be preserved without scanning the case base. For this reason, the latter must undergo maintenance operations. The techniques of case base's dimension optimization is the analog of instance reduction size methodology (in the machine learning community). This study links these techniques by presenting case-based maintenance in the framework of instance based reduction, and provides: first an overview of CBM studies, second, a novel method of structuring and updating the case base and finally an application of industrial case is presented.The structuring combines a categorization algorithm with a measure of competence CM based on competence and performance criteria. Since the case base must progress over time through the addition of new cases, an auto-increment algorithm is installed in order to dynamically ensure the structuring and the quality of a case base. The proposed method was evaluated through a case base from an industrial plant. In addition, an experimental study of the competence and the performance was undertaken on reference benchmarks. This study showed that the proposed method gives better results than the best methods currently found in the literature.

show abstract

“…There are numerous new research studies on the first type of method, including the MC-SVMA model [8], the K-class support vector classification-regression (K-SVCR) method [9], the learning vector quantization (LVQ) method [10], the improving prototype selection k-nearest neighbor (PS-KNN) method [11], Bayesian network-based chain classifiers [12], the BPSO-AdaBoost-KNN ensemble learning algorithm [13], and the hyper-sphere multi-class SVM (HSMC-SVM) method [14]. Among these, the algorithm that improved over the SVM principle for classifying multiple targets is the most widely applied.…”

Section: Introductionmentioning

confidence: 99%

Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks

Zhang

Pan

2018

Sensors

View full text Add to dashboard Cite

A novel multi-class classification method named the voting-cross support vector machine (SVM) method was proposed in this study, for classifying vehicle targets in wireless sensor networks. The advantages and disadvantages of available methods were summarized, after a comparative analysis of commonly used multi-objective classification algorithms. To improve the classification accuracy of multi-class classification and ensure the low complexity of the algorithm for engineering implementation on wireless sensor network (WSN) nodes, a framework was proposed for cross-matching and voting on the category to which the vehicle belongs after combining the advantages of the directed acyclic graph SVM (DAGSVM) method and binary-tree SVM method. The SVM classifier was selected as the basis two-class classifier in the framework, after comparing the classification performance of several commonly used methods. We utilized datasets acquired from a real-world experiment to validate the proposed method. The calculated results demonstrated that the cross-voting SVM method could effectively increase the classification accuracy for the classification of multiple vehicle targets, with a limited increase in the algorithm complexity. The application of the cross-voting SVM method effectively improved the target classification accuracy (by approximately 7%), compared with the DAGSVM method and the binary-tree SVM method, whereas time consumption decreased by approximately 70% compared to the DAGSVM method.

show abstract

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Cited by 63 publications

References 26 publications

Prototype generation on structural data using dissimilarity space representation

Prototype generation on structural data using dissimilarity space representation

Case-based maintenance: Structuring and incrementing the case base

Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks

Contact Info

Product

Resources

About