The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data

Villuendas-Rey, Yenny; Benguría, Carmen Rey; Ferreira-Santiago, Ángel; Camacho-Nieto, Oscar; Yáñez-Márquez, Cornelio

doi:10.1016/j.neucom.2017.03.085

Cited by 36 publications

(37 citation statements)

References 44 publications

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“…Preliminaries Table II shows the Alpha and Beta operators, which were introduced in [23,24]. They are the basis for the development of Alpha-Beta memories, the Gamma classifier, passing through models such as the Alpha-Beta-BAM [24], the Alpha-Beta support vector machines [25], and many other models [26][27][28][29][30][31][32][33][34][35].…”

Section: Table-ii: Alpha and Beta Operatorsmentioning

confidence: 99%

Sensing and Forecasting of Pollution Data in Mexico City

Molina,

Cruz,

Vega

2019

IJEAT

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In this paper we present the characteristics of sensors used to monitor the pollution levels in Mexico City, namely sulfur dioxide (SO2), nitrogen oxides (NOx), ozone (O3), , and carbon monoxide (CO). A novel algorithm to predict contamination levels is presented: the Gamma classifier. Also, a new coding technique is introduced, allowing the conversion from a series of values taken from SIMAT databases into a set of patterns, which in turn are useful for the task of pollutant forecasting. Experimental results show a competitive performance by the Gamma classifier as a predictor, when compared to other methods.

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Section: Table-ii: Alpha and Beta Operatorsmentioning

confidence: 99%

Sensing and Forecasting of Pollution Data in Mexico City

Molina,

Cruz,

Vega

2019

IJEAT

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“…Usually the information about instances is given in a matrix form MI = (X j (O i ))m x n with m rows (instance descriptions) and n columns (values of each attribute) [17,18].…”

Section: Previous Workmentioning

confidence: 99%

Using Extended Compact Sets to Cluster Educational Data

Rey-Benguría*

2019

IJITEE

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The pedagogical orientation to the families of children with behavioral deficiencies needs a differentiated approach, accorded to the particular characteristics of each family. To accomplish this task, the personnel in charge of family orientation in the Schools for children with affective-behavioral maladies, diagnose the families, to obtain the peculiarities of the familiar dynamics. After that, they need to obtain groups of similar families, in order to carry out a better orientation. The data of the familiar dynamics are mixed an incomplete, and the desired group number is not given. In this paper we extend the  0-compact sets structuralization to cluster the families according to their characteristics. We found five groups, each of them with distinctive familiar dynamics. With the five groups, the family orientation specialists designed a personalized strategy with a more coherent and adequate orientation.

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“…The AntTree is based on modeling the ability of ants to build living structures with their bodies [23], to discover, in a distributed and unsupervised manner, a tree structure that organizes a set of data. This hierarchical structure can be interpreted in several ways: as a partition of the data or as a hierarchical structuring of them [23,[60][61][62][63][64][65].…”

Section: Pantsa In the Improvement Of Clustersmentioning

confidence: 99%

PANTSA Influence in grouping Mixed and Incomplete Data

Chávez-Castilla*

2019

IJITEE

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Obtaining high quality groups and processing mixed and incomplete data (DMI) are still problems in the data clustering. Recently a method was proposed that improves the results obtained by clustering algorithms, the PAntSA; but this was only designed and tested for numerical data. For this reason, this paper analyzes the influence of applying the PAntSA in the performance of DMI restricted clustering algorithms. For this, the results of different algorithms are compared before and after applying the PAntSA. The comparisons made provide experimental evidence that the PAntSA algorithm improves the quality of the groups obtained by traditional DMI clustering methods.

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The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data

Cited by 36 publications

References 44 publications

Sensing and Forecasting of Pollution Data in Mexico City

Sensing and Forecasting of Pollution Data in Mexico City

Using Extended Compact Sets to Cluster Educational Data

PANTSA Influence in grouping Mixed and Incomplete Data

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