Towards improving decision tree induction by combining split evaluation measures

Loyola‐González, Octavio; Ramírez-Sáyago, Ernesto; Medina‐Pérez, Miguel Angel

doi:10.1016/j.knosys.2023.110832

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…A Decision Tree is a supervised learning algorithm widely used in data mining due to its simplicity and ease of interpretation Loyola-González et al [2023]. This algorithm operates on a set of logical rules, requiring minimal parameter tuning to process diverse data types effectively Su and Zhang [2006].…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Gene Sequence to 2D Vector Transformation for Virus Classification

Sanchez-Gendriz,

Azevedo,

de Souza

et al. 2024

Preprint

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Background: DNA sequences harbor vital information regarding various organisms and viruses. The ability to analyze extensive DNA sequences using methods amenable to conventional computer hardware has proven invaluable, especially in timely response to global pandemics such as COVID-19. Objectives: This study introduces a new representation that encodes DNA sequences in unit vector transitions in a 2D space, extracted from the 2019 repository Novel Coronavirus Resource (2019nCoVR). The main objective is to elucidate the potential of this method to facilitate virus classification using minimal hardware resources. It also aims to demonstrate the feasibility of the technique through dimensionality reduction and the application of machine learning models. Methods: DNA sequences were transformed into two-nucleotide base transitions (referred to as 'transitions'). Each transition was represented as a corresponding unit vector in 2D space. This coding scheme allowed DNA sequences to be efficiently represented as dynamic transitions. After applying a moving average and resampling, these transitions underwent dimensionality reduction processes such as Principal Component Analysis (PCA). After subsequent processing and dimensionality reduction, conventional machine learning approaches were applied, obtaining as output a multiple classification among six species of viruses belonging to the coronaviridae family, including SARS-CoV-2. Results and Discussions: The implemented method effectively facilitated a careful representation of the sequences, allowing visual differentiation between six types of viruses from the Coronaviridae family through direct plotting. The results obtained by this technique reveal values accuracy, sensitivity, specificity and F1-score equal to or greater than 99 %, applied in a stratified cross-validation, used to evaluate the model. The results found produced performance comparable, if not superior, to the computationally intensive methods discussed in the state of the art. Conclusions: The proposed coding method appears as a computationally efficient and promising addition to contemporary DNA sequence coding techniques. Its merits lie in its simplicity, visual interpretability and ease of implementation, making it a potential resource in complementing existing strategies in the field.

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Section: Decision Tree (Dt)mentioning

confidence: 99%

Gene Sequence to 2D Vector Transformation for Virus Classification

Sanchez-Gendriz,

Azevedo,

de Souza

et al. 2024

Preprint

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“…Keempat, memahami hubungan sebabakibat dalam analisis observasional bisa menjadi sulit dan memerlukan perhatian ekstra terhadap faktor-faktor yang tidak terdeteksi (Han et al, 2023). Kelima, perubahan konteks dari waktu ke waktu dapat mempengaruhi dinamika faktor-faktor tersebut (Loyola-González et al, 2023). Keenam, hasil analisis yang berhasil di satu institusi belum tentu dapat diterapkan begitu saja pada institusi lain karena perbedaan karakteristik (Y.…”

Section: Pendahuluanunclassified

Exploring Complex Decision Trees: Unveiling Data Patterns and Optimal Predictive Power

Setiawan,

Fina Antika Cahyani,

Sadida

2023

j.innov.future.technology

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This research investigates the development and analysis of decision tree models in the context of classification tasks. Decision tree models were developed without employing pruning or pre-pruning techniques and were tested on relevant datasets. The research findings demonstrate that complex models without pruning achieved the highest level of accuracy in classifying data. This study was inspired by the potential issue of students facing the risk of not completing their studies (dropout), which could lead to a decline in the college's accreditation rating. Therefore, this model was devised to assist in identifying factors that could influence this outcome as a preventative measure. Additionally, we successfully generated clear visualizations of the decision trees, enhancing the understanding of the model's decision-making process. This research provides insights into the adaptability of decision tree models within this specific case and showcases their potential for enhancing decision-making across various contexts. These findings encourage further discussions on the benefits of pruning methods within this specific context and the broader application potential of decision tree models.

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“…C-band SAR data from S-1 are unaffected by cloudy and rainy conditions due to its high penetration and its ability to acquire data at nighttime, as well as its sensitivity to changes in vegetation structure, compared with optical remote sensing data [30,31]. Many studies have demonstrated the efficacy of using S-1 data to identify annual crops' (e.g., soybean, corn, and rice) phenology [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics

Han,

Wang,

et al. 2024

Agronomy

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The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as Hv (high valley value of the 14 May, 26 May, and 7 June SAR data), Tb (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and Lo (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.

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Towards improving decision tree induction by combining split evaluation measures

Cited by 7 publications

References 47 publications

Gene Sequence to 2D Vector Transformation for Virus Classification

Gene Sequence to 2D Vector Transformation for Virus Classification

Exploring Complex Decision Trees: Unveiling Data Patterns and Optimal Predictive Power

A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics

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