A nonparametric classification method based on K-associated graphs

Bertini, João Roberto; Zhao, Liang; Motta, Rosângela dos Santos; Lopes, Alneu de Andrade

doi:10.1016/j.ins.2011.07.043

Cited by 55 publications

(64 citation statements)

References 23 publications

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“…In this way, an algorithm, which is able to adapt itself to different classes of the data set is welcome. The idea of KAOG is to obtain the optimal K value for each component in order to maximize its purity [8].…”

Section: K-associated Optimal Graphmentioning

confidence: 99%

“…In the first lines, K starts with the value 1 and, thus, the 1-associated graph is considered as the optimal graph (G opt ) at this moment. After the initial setting, a loop starts to merge the subsequent K-associated graphs by increasing K, while improving the purity of the network encountered so far, until the optimal network measured by the purity degree [8] is reached. Between lines 7 and 12, the algorithm verifies for each component of the K-associated graph (C (K) β ) whether the condition given by line eight is satisfied.…”

Section: K-associated Optimal Graphmentioning

confidence: 99%

“…Another functions such as find the graph components and compute the purity measure of the graph components yield the complexity order of O(N). Therefore, the time complexity to build up the network is O(N 2 ) [8].…”

Section: Algorithm 2 K -Associated Optimal Graphmentioning

confidence: 99%

“…However, most of the researches related to complex networks are applied to data clustering, dimensionality reduction and semi-supervised learning [20][21][22]. Recently, some network-based techniques have been proposed to solve supervised learning problems, such as data classification [8,10,11,23,24]. The obtained results show that network-based techniques have advantages over traditional ones in many aspects, such as the ability to detect classes of different shapes, absence of parameters, and the ability to classify data according to pattern formation of the training data.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], the authors proposed a network-based classification algorithm, called K-associated optimal graph (KAOG). Among other characteristics, KAOG constructs a network through a local optimization structure based on a index of purity, which measures the compactness of the graph components.…”

Section: Introductionmentioning

confidence: 99%

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Network-based data classification: combining K-associated optimal graphs and high-level prediction

et al. 2014

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Background: Traditional data classification techniques usually divide the data space into sub-spaces, each representing a class. Such a division is carried out considering only physical attributes of the training data (e.g., distance, similarity, or distribution). This approach is called low-level classification. On the other hand, network or graph-based approach is able to capture spacial, functional, and topological relations among data, providing a so-called high-level classification. Usually, network-based algorithms consist of two steps: network construction and classification. Despite that complex network measures are employed in the classification to capture patterns of the input data, the network formation step is critical and is not well explored. Some of them, such as K-nearest neighbors algorithm (KNN) and -radius, consider strict local information of the data and, moreover, depend on some parameters, which are not easy to be set. Methods: We propose a network-based classification technique, named high-level classification on K-associated optimal graph (HL-KAOG), combining the K-associated optimal graph and high-level prediction. In this way, the network construction algorithm is non-parametric, and it considers both local and global information of the training data. In addition, since the proposed technique combines low-level and high-level terms, it classifies data not only by physical features but also by checking conformation of the test instance to formation pattern of each class component. Computer simulations are conducted to assess the effectiveness of the proposed technique. Results:The results show that a larger portion of the high-level term is required to get correct classification when there is a complex-formed and well-defined pattern in the data set. In this case, we also show that traditional classification algorithms are unable to identify those data patterns. Moreover, computer simulations on real-world data sets show that HL-KAOG and support vector machines provide similar results and they outperform well-known techniques, such as decision trees and K-nearest neighbors. Conclusions:The proposed technique works with a very reduced number of parameters and it is able to obtain good predictive performance in comparison with traditional techniques. In addition, the combination of high level and low level algorithms based on network components can allow greater exploration of patterns in data sets.

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Section: K-associated Optimal Graphmentioning

confidence: 99%

Section: K-associated Optimal Graphmentioning

confidence: 99%

Section: Algorithm 2 K -Associated Optimal Graphmentioning

confidence: 99%