Body Weight Prediction of Thalli Sheep Reared in Southern Punjab Using Different Data Mining Algorithms

This study is conducted to predict the body weight (BW) for Thalli sheep of southern Punjab from different body measurements. In the BW prediction, several body measurements viz., withers height, body length, head length, head width, ear length, ear width, neck length, neck width, heart girth, rump length, rump width, tail length, barrel depth and sacral pelvic width are used as predictors. The data mining algorithms such as Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CART) and Artificial Neural Network (ANN) are used to predict the BW for a total of 85 female Thalli sheep. The data set is partitioned into training (80 %) and test (20 %) sets before the algorithms are used. The minimum number of parent (4) and child nodes (2) are set in order to ensure their predictive ability. The R2 % and RMSE values for CHAID, Exhaustive CHAID, ANN and CART algorithms are 67.38(1.003), 64.37(1.049), 61.45(1.093) and 59.02(1.125), respectively. The mostsignificant predictor is BL in the BW prediction of Thalli sheep. The heaviest BW average of 9.596 kg is obtained from the subgroup of those having BL > 25.000 inches. On behalf of the several goodness of fit criteria, we conclude that the CHAID algorithm performance is better in order to predict the BW of Thalli sheep and more suitable decision tree diagram visually. Also, the obtained CHAID results may help to determine body measurements positively associated with BW for developing better selection strategies with the scope of indirect selection criteria.

Penerapan dan Perbandingan Tiga Metode Analisis Pohon Keputusan pada Klasifikasi Penderita Kanker Payudara

Today there is a considerable amount of work dealing with decision trees, especially in survival analysis (Ibrahim et al, 2008). Cases classified as survival analysis, like cancer patients. This study discusses the application of data mining which is to obtain diagnostic results. The classification technique uses information obtained from medical records of breast cancer patients in Yugoslavia. A method for answering these problems through decision tree analysis using the CHAID, Exhaustive CHAID and CART methods. Empirically aiming to compare performance of three decision tree classification methods so that the best method is obtained. It was concluded that best method used in applying to the classification of breast cancer sufferers was the CART method because it was able to get the most significant variables at most four, namely inv-node, tumor size, deg-malig and breast parts. Then it has a total accuracy rate with highest value of 84.9 percent and has a total error rate with lowest value of 15.1 percent. Abstrak. Dewasa ini ada cukup banyak pekerjaan yang berurusan dengan pohon keputusan, terutama dalam analisis survival (Ibrahim dkk, 2008). Kasus yang tergolong analisis survival seperti penderita penyakit kanker. Penelitian ini membahas mengenai penerapan data mining yang digunakan untuk mendapatkan hasil diagnostik. Pendekatan teknik klasifikasi dengan menggunakan informasi yang diperoleh pada rekam medis data penderita kanker payudara di Yugoslavia. Salah satu metode untuk menjawab permasalahan tersebut melalui analisis pohon keputusan dengan metode CHAID, Exhaustive CHAID dan CART. Secara empiris bertujuan untuk membandingkan kinerja tiga metode pengklasifikasi pohon keputusan agar didapatkan metode manakah yang terbaik. Maka disimpulkan bahwa metode terbaik yang digunakan dalam penerapan pada klasifikasi penderita kanker payudara adalah metode CART sebab mampu mendapatkan variabel signifikan yang paling banyak ada empat, yakni inv-node, ukuran tumor, deg-malig dan bagian payudara. Kemudian memiliki tingkat akurasi total dengan nilai tertinggi sebesar 84.9 persen dan memiliki total tingkat kesalahan dengan nilai yang terendah sebesar 15.1 persen.

Phenotypic Characterization of Hair and Honamli Goats by Using Classification Trees Algorithms and Multivariate Adaptive Regression Splines (Mars)

In order to meet the food demand of the increasing world population, it is very important to define the animal breeds and species raised in tropical and subtropical regions and to organize breeding programs for this. Discrimination animal breeds by morphological classification are a widely used method for a century. Although Honamli and Hair goats are very similar to each other morphologically, they can be subjectively distinguished by experienced breeders with some distinctive morphological markers. In the current study, certain body characteristics of Hair goats, which have a large portion of the population in Turkey, and Honamli goat, which has recently been registered as a new breed were used. Phenotypic characterization of these breeds has been made using data mining methods such as Classification and regression tree (CART), chi-square automatic interaction detector (CHAID), Exhaustive CHAID, Quick Unbiased, Efficient Statistical Tree, (QUEST), and multivariate adaptive regression splines (MARS) algorithms. In other words, the current study is the first data mining algorithms used for phenotypic characterization in Hair and Honamli goat breeds. Goats’ morphological characteristics such as live weight (LW), withers height (WH), back height (BH), rump height (RH), chest Depth (CD), body length (BL), chest girth (CG), leg girth (LG), head length (HL), fore head (FH), ear length (EL), and tail length (TL), used in diagnosis of discrimination on breeds, were used as a binary response variable in Honamli and Hair breeds. Here, the independent variables used in data mining algorithms are the morphological characteristics of goats. CHAID, Exhaustive CHAID, CART, QUEST, and MARS were used as data mining algorithms to make an accurate decision in detecting effective morphological traits in breed discrimination. The success of the CHAID, Exhaustive CHAID, CART, QUEST and MARS algorithms in breed discrimination is 87.80%, 85.80%, 87.80%, 77.00%, and 88.51%, respectively, while the area under the ROC curve is 0.880, 0.853, 0.868, 0.784 and 0.942, respectively. As a result, using data mining methods for some body measurements of Honamli and Hair goats, whose morphological distinction is not exactly accurate, phenotype characterization separation was performed with high success in MARS and CHAID algorithms compared with the other methods. The outputs of this study can be used for breeding material by enabling pure Honamli goat breeding. Also, data mining algorithms can be included in gene resource conservation programs.

Go/No-Go Decision Model for Owners Using Exhaustive CHAID and QUEST Decision Tree Algorithms

Go/no-go execution decisions are one of the most important strategic decisions for owners during the early stages of construction projects. Restructuring the process of decision-making during these early stages may have sustainable results in the long run. The purpose of this paper is to establish proper go/no-go decision-tree models for owners. The decision-tree models were developed using Exhaustive Chi-square Automatic Interaction Detector (Exhaustive CHAID) and Quick, Unbiased, Efficient Statistical Tree (QUEST) algorithms. Twenty-three go/no-go key factors were collected through an extensive literature review. These factors were divided into four main risk categories: organizational, project/technical, legal, and financial/economic. In a questionnaire distributed among the construction professionals, the go/no-go variables were asked to be ranked according to their perceived significance. Split-sample validation was applied for testing and measuring the accuracy of the Exhaustive CHAID and QUEST models. Moreover, Spearman’s rank correlation and analysis of variance (ANOVA) tests were employed to identify the statistical features of the 100 responses received. The result of this study benchmarks the current assessment models and develops a simple and user-friendly decision model for owners. The model is expected to evaluate anticipated risk factors in the project and reduce the level of uncertainty. The Exhaustive CHAID and QUEST models are validated by a case study. This paper contributes to the current body of knowledge by identifying the factors that have the biggest effect on an owner’s decision and introducing Exhaustive CHAID and QUEST decision-tree models for go/no-go decisions for the first time, to the best of the authors’ knowledge. From the “sustainability” viewpoint, this study is significant since the decisions of the owner, based on a rigorous model, will yield sustainable and efficient projects.

Методика экспресс-диагностики риска подверженности курсантов негативному информационно-психологическому воздействию

На основе алгоритма деревьев классификации Exhaustive CHAID с использованием результатов экспериментальных исследований разработана методика экспресс-диагностики риска подверженности курсантов негативному информационно-психологическому воздействию. Предлагаемая экспресс-методика обеспечивает сокращение временных затрат по сравнению со стандартным набором методик при допустимом качестве классификации.

Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea

We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.

Evaluation of farmers’ knowledge on the rare Abyssinian pea

Gebreegziabher BG, Tsegay BA. 2018. Evaluation of farmers’ knowledge on the rare Abyssinian pea (Pisum sativum var. abyssinicum) landraces of Ethiopia. Biodiversitas 19: 1851-1865. Abyssinian pea (Pisum sativum var. abyssinicum A. Braun) is a rare and problematic taxon requiring evaluation of present farmers’ local knowledge. Cross-sectional data were collected from 444 respondents and analyzed using SPSS software. Descriptive statistics was used; one way ANOVA for significance test of variance and Exhaustive CHAID growth method for predictions. Prediction results showed that the crop requires about two good rains, Nitisol soils and about 21-30 kg ha-1seeding rate. The flowering to maturity time ranges 11∕2 to 21∕2 months depending on the agroecology (highland or lowland), with a yield of about 300-400 kg ha-1on average. The crop distribution is currently limited to three to four districts and sown after other crops are harvested. Major factors hindering its distribution are agro-ecological suitability, lack of intervention and preference of high yielding pea varieties. The crops’ inferiority in yield and pest susceptibility is the main reason for less extensive awareness on the crop. Though inferior in yield and susceptible to pest, farmers still prefer to grow the crop because of its marketability for local exchange and consumption. The core production problems currently remarked by farmers are expensive price of the seed to buy and small land holding.

Modeling and Testing Landslide Hazard Using Decision Tree

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.