scholarly journals Decision tree analysis for the determination of relevant variables and quantifiable reference points to establish maturity stages in Enteroctopus megalocyathus and Illex argentinus

2015 ◽  
Vol 72 (5) ◽  
pp. 1449-1461 ◽  
Author(s):  
Augusto César Crespi-Abril ◽  
Nicolás Ortiz ◽  
David Edgardo Galván
Keyword(s):  
Decision Tree ◽  
Gonadosomatic Index ◽  
Reference Points ◽  
Maturity Stage ◽  
Maturity Stages ◽  
Terminal Organ ◽  
Nidamental Gland ◽  
Relevant Variables ◽  
Illex Argentinus

Abstract Determining the maturity condition of a large number of individuals is crucial for stock assessment and management of cephalopod populations, but this task is difficult to conduct in practice. We propose a novel approach for maturity stage classification using observer-independent criteria. Relevant morphological variables for classification are determined via decision tree (DT) analysis. Using Illex argentinus and Enteroctopus megalocyathus as case studies, individuals were sexed and assigned to a maturity stage defined by specific macroscopic maturity scales. Also, for each individual, the weight of the gonad, accessory glands/ducts, mantle length, and total weight were recorded and maturity indices were calculated (Hayashi index and gonadosomatic index). Two different DT models were fitted: one considering all maturity stages and the other considering only intermediate maturity stages since these are the most difficult to determine in practice. For the classification of I. argentinus among all stages, the weights of the nidamental gland and oviducts were the most relevant variables for females (misclassification 23%), while spermatophoric complex and testis weights were the key variables for males (misclassification 23%). For classification of intermediate stages only, the nidamental gland and spermatophoric complex weights were the most relevant variables to classify females (misclassification 19%) and males (misclassification 21%), respectively. For E. megalocyathus, the oviducts and ovary weights of females and the terminal organ weight of males were the most relevant variables for classification among all maturity stages (misclassification 16% and 18%, respectively). For intermediate maturity stages, the same variables were most important and misclassification improved to 13% for both sexes. Gonadosomatic and Hayashi's indices were not relevant in either model. DTs based on measurements of cephalopod reproductive systems revealed a simple classification system for maturity stages using only a few variables that are easy to measure in the field and are independent of observer training.

Identification of Spring- and Autumn-Spawning Herring (Clupea harengus harengus) using Maturity Stages Assigned from a Gonadosomatic Index Model

1989 ◽  
Vol 46 (6) ◽  
pp. 969-980 ◽  
Author(s):  
Ian H. McQuinn
Keyword(s):  
Gonadosomatic Index ◽  
Indirect Evidence ◽  
Clupea Harengus ◽  
Identification Accuracy ◽  
Maturity Stage ◽  
Index Model ◽  
Clupea Harengus Harengus ◽  
Type Classification ◽  
Meristic Data

A model is developed to identify the spawning type of individual herring (Clupea harengus harengus) from the sympatric spring and autumn spawning stocks in the southern Gulf of St. Lawrence (NAFO Division 4T). The maturity stage is assigned from a gonadosomatic index model using discriminant analysis and the spawning type is determined from the maturity stage and the month of capture according to a detailed description of the annual maturity cycle of each type. The model is shown to be objective, quantitative, and precise. Greater than 97% overall agreement was achieved between the spawning type classification of individuals by histological means and by the model, significantly improving the identification accuracy compared with a macroscopic maturity stage key. The model also has the advantages of (1) identifying the spawning type directly by determining the "season of spawning" rather than the "season spawned" as is inferred from indirect evidence such as otolith characteristics or meristic data, (2) requiring easily obtained, quantifiable data, and (3) enabling the rapid treatment of a large number of specimens.

scholarly journals Decision Tree C 4.5 Algorithm for Classification of Poor Family Scholarship Recipients

2021 ◽  
Vol 1125 (1) ◽  
pp. 012048
Author(s):  
Y Kustiyahningsih ◽  
B K Khotimah ◽  
D R Anamisa ◽  
M Yusuf ◽  
T Rahayu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Poor Family ◽  
Scholarship Recipients

scholarly journals 174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 126-127
Author(s):  
Lucas S Lopes ◽  
Christine F Baes ◽  
Dan Tulpan ◽  
Luis Artur Loyola Chardulo ◽  
Otavio Machado Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Final Decision ◽  
Relevant Parameter ◽  
Good Prediction ◽  
Quality Traits ◽  
C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

scholarly journals Classification of diabetes disease using decision tree algorithm (C4.5)

2021 ◽  
Vol 1869 (1) ◽  
pp. 012082
Author(s):  
B A C Permana ◽  
R Ahmad ◽  
H Bahtiar ◽  
A Sudianto ◽  
I Gunawan
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

A Decision Tree Can Increase Accuracy When Assessing Curve Types According to Lenke Classification of Adolescent Idiopathic Scoliosis

Spine ◽  
2010 ◽  
Vol 35 (10) ◽  
pp. 1054-1059 ◽  
Author(s):  
Philippe Phan ◽  
Neila Mezghani ◽  
Marie-Lyne Nault ◽  
Carl-Éric Aubin ◽  
Stefan Parent ◽  
...  
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Decision Tree ◽  
Idiopathic Scoliosis ◽  
Lenke Classification

scholarly journals Ripening of "Pedro Sato" guava: study on its climacteric or non-climacteric nature

2005 ◽  
Vol 17 (3) ◽  
pp. 299-306 ◽  
Author(s):  
Marisa Azzolini ◽  
Angelo Pedro Jacomino ◽  
Ilana Urbano Bron ◽  
Ricardo Alfredo Kluge ◽  
Marlene Aparecida Schiavinato
Keyword(s):  
Respiratory Rate ◽  
Ethylene Production ◽  
Room Temperature ◽  
Psidium Guajava ◽  
Maturity Stage ◽  
Physiological Basis ◽  
Tropical Fruit ◽  
Maturity Stages ◽  
Climacteric Fruit ◽  
Light Green

Guava (Psidium guajava L.) is a tropical fruit exhibiting rapid post-harvest ripening. However, the physiological basis involved in the ripening process of guava is not totally clear, which makes it difficult to develop technologies to enhance fruit storability. Two experiments were carried out with the objective of determining the ripening behavior of 'Pedro Sato' guavas. In the first experiment, guava fruits at three maturity stages (I - dark green, II - light green and III - yellow-green) were stored at room temperature (23 ± 1°C and 85 ± 5 % RH). The respiratory rate, ethylene production, pulp and skin colours, and firmness were evaluated. In the second experiment, ethylene and 1-methylcyclopropene (1-MCP) were applied to guavas at the light green maturity stage and the ripening behaviour during storage at room temperature was studied. Fruits from all maturity stages showed a gradual increase in the respiratory rate and ethylene production. The intense changes in pulp and skin colours and in firmness preceded the maximum respiratory rate and ethylene production. 1-MCP reduced the rate of ripening, while the application of ethylene did not promote this process. These results do not permit the classification of 'Pedro Sato' guava as a traditional climacteric fruit.

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