Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier

<p>Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User&#8217;s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.</p><p>Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest</p>

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Detecting Fake News Tweets from Twitter

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/08428 ◽

2021 ◽

Vol 23 (08) ◽

pp. 532-537

Author(s):

Cherlakola Abhinav Reddy ◽

◽

Sai Nitesh Gadiraju ◽

Dr. Samala Nagaraj ◽

◽

...

Keyword(s):

Support Vector Machines ◽

Random Forest ◽

Decision Tree ◽

Random Forest Classifier ◽

Support Vector ◽

Online Media ◽

Fake News ◽

Breaking News ◽

Learning Techniques ◽

Vector Machines

Online media has progressively obtained integral to the route billions of individuals experience news and occasions, frequently bypassing writers—the conventional guardians of breaking news. Occasions,in reality, make a relating spike of posts (tweets) on Twitter. This projects a great deal of significance on the validity of data found via online media stages like Twitter. We have utilized different managed learning techniques like Naïve Bayes, Decision Trees, and Support Vector Machines on the information to separate tweets among genuine and counterfeit news. For our AI models, we have utilized tweet and client highlights as our indicators. We accomplished a precision of 88% utilizing the Random Forest classifier and 88% utilizing the Decision tree. Notwithstanding, we accept that breaking down client records would build the accuracy of our models.

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Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

Applications of Big Data in Large- and Small-Scale Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-6673-2.ch015 ◽

2021 ◽

pp. 234-259

Author(s):

Nitika Kapoor ◽

Parminder Singh

Keyword(s):

Feature Extraction ◽

Heart Disease ◽

Random Forest ◽

Decision Tree ◽

Random Forest Classifier ◽

Disease Prediction ◽

Decision Tree Classifier ◽

Hybrid Classifier ◽

Forest Classification ◽

Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

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Random Forest: A Hybrid Implementation for Sarcasm Detection in Public Opinion Mining

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3758.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 5022-5025

Keyword(s):

Decision Making ◽

Public Opinion ◽

Random Forest ◽

Decision Tree ◽

Opinion Mining ◽

Random Forest Classifier ◽

Decision Tree Classifier ◽

Wrong Decision ◽

Tree Classifier ◽

Better Than

Modelling the sentiment with context is one of the most important part in Sentiment analysis. There are various classifiers which helps in detecting and classifying it. Detection of sentiment with consideration of sarcasm would make it more accurate. But detection of sarcasm in people review is a challenging task and it may lead to wrong decision making or classification if not detected. This paper uses Decision Tree and Random forest classifiers and compares the performance of both. Here we consider the random forest as hybrid decision tree classifier. We propose that performance of random forest classifier is better than any other normal decision tree classifier with appropriate reasoning

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Sickle cell segmentation and classification for thalassemia aid diagnosis

F1000Research ◽

10.12688/f1000research.73314.1 ◽

2021 ◽

Vol 10 ◽

pp. 1185

Author(s):

Yen-Siang Leow ◽

Kok-Why Ng ◽

Yih-Jian Yoong ◽

Seng-Beng Ng

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Decision Tree ◽

Complete Blood Count ◽

Similarity Index ◽

Random Forest Classifier ◽

The Body ◽

Cell Segmentation ◽

Support Vector ◽

Morphological Operations

Background: Thalassemia is a hereditary blood disease in which abnormal red blood cells (RBCs) carry insufficient oxygen throughout the body. Conventional methods of thalassemia detection through a complete blood count (CBC) test and peripheral blood smear image still possess a lot of weaknesses. Methods: This paper proposes a hybrid segmentation method to segment the RBCs. It incorporates adaptive thresholding and canny edge method to segment the RBCs. Morphological operations are performed to clean the leftovers. Shape and texture features are extracted using the segmented masks and the gray level co-occurrence matrix. Data imbalance treatment is used for solving the imbalance cell type class in distribution. In the data resampling layer, the synthetic minority oversampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and random over sampling (ROS) are performed and evaluated using the decision tree and logistic regression. In the classification layer, the decision tree, random forest classifier and support vector machine (SVM) are assessed and compared for the best performance in classification. Results:The proposed method outperforms the other methods in the image segmentation layer with the structural similarity index measure (SSIM) of 89.88%. In the data resampling layer, ADASYN is employed as it is more accurate than the SMOTE and ROS. The random forest classifier is chosen at the classification layer as it is more accurate than the decision tree and support vector machine (SVM). Conclusions:The proposed method is tested on the latest dataset of erythrocyteIDB3 and it solves the issues of imbalanced data due to the insufficient cell classes.

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Using Decision Tree Aggregation with Random Forest Model to Identify Gut Microbes Associated with Colorectal Cancer

Genes ◽

10.3390/genes10020112 ◽

2019 ◽

Vol 10 (2) ◽

pp. 112 ◽

Cited By ~ 11

Author(s):

Dongmei Ai ◽

Hongfei Pan ◽

Rongbao Han ◽

Xiaoxin Li ◽

Gang Liu ◽

...

Keyword(s):

Colorectal Cancer ◽

Random Forest ◽

Decision Tree ◽

Scientific Data ◽

Random Forest Classifier ◽

Colorectal Cancers ◽

Human Gut ◽

Gut Microbes ◽

Colorectal Cancer Patients ◽

Consensus Decision

The imbalance of human gut microbiota has been associated with colorectal cancer. In recent years, metagenomics research has provided a large amount of scientific data enabling us to study the dedicated roles of gut microbes in the onset and progression of cancer. We removed unrelated and redundant features during feature selection by mutual information. We then trained a random forest classifier on a large metagenomics dataset of colorectal cancer patients and healthy people assembled from published reports and extracted and analysed the information from the learned decision trees. We identified key microbial species associated with colorectal cancers. These microbes included Porphyromonas asaccharolytica, Peptostreptococcus stomatis, Fusobacterium, Parvimonas sp., Streptococcus vestibularis and Flavonifractor plautii. We obtained the optimal splitting abundance thresholds for these species to distinguish between healthy and colorectal cancer samples. This extracted consensus decision tree may be applied to the diagnosis of colorectal cancers.

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Classification of Malicious Web Pages through a J48 Decision Tree, a Naïve Bayes, a RBF Network and a Random Forest Classifier for WebSpam Detection

International Journal of u- and e- Service Science and Technology ◽

10.14257/ijunesst.2017.10.4.05 ◽

2017 ◽

Vol 10 (4) ◽

pp. 51-72

Author(s):

Muhammad Iqbal ◽

Malik Muneeb Abid ◽

Usman Waheed ◽

Syed Hasnain Alam Kazmi

Keyword(s):

Random Forest ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Random Forest Classifier ◽

Web Pages ◽

Rbf Network ◽

J48 Decision Tree

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Heart Disease Prediction Method using Hybrid Classifier

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1109.0789s419 ◽

2019 ◽

Vol 8 (9S4) ◽

pp. 57-61

Keyword(s):

Feature Extraction ◽

Heart Disease ◽

Random Forest ◽

Decision Tree ◽

Research Work ◽

Random Forest Classifier ◽

Disease Prediction ◽

Decision Tree Classifier ◽

Hybrid Classifier ◽

Tree Classifier

The data mining is the approach which can extract useful information from the data. The following research work that has been described is related to the heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. For the heart disease prediction the classifier that is designed in this research work is hybrid classifier. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps which are data pre-processing, feature extraction and classification. In this paper, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. We have proposed a hybrid model that has been implemented in python. Moreover, the results are compared with Support Vector Machine (SVM) and K-Nearest Neighbor classifier (KNN).

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