Query: the Random Forest classiﬁers  text

title: A bias variance analysis of state of the art random forest text classifiers
abstract: Random forest (RF) classifiers do excel in a variety of automatic classification tasks, such as topic categorization and sentiment analysis. Despite such advantages, RF models have been shown to perform poorly when facing noisy data, commonly found in textual data, for instance. Some RF variants have been proposed to provide better generalization capabilities under such challenging scenario, including lazy, boosted and randomized forests, all which exhibit significant reductions on error rate when compared to the traditional RFs. In this work, we analyze the behavior of such variants under the bias variance decomposition of error rate. Such an analysis is of utmost importance to uncover the main causes of the observed improvements enjoyed by those variants in classification effectiveness. As we shall see, significant reductions in variance along with stability in bias explain a large portion of the improvements for the lazy and boosted RF variants. Such an analysis also sheds light on new promising directions for further enhancements in RF based learners, such as the introduction of new randomization sources on both, lazy and boosted variants.

title: A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification
abstract: In the current generation, a huge amount of textual documents are generated and there is an urgent need to organize them in a proper structure so that classification can be performed and categories can be properly defined. The key technology for gaining the insights into a text information and organizing that information is known as text classification. The classes are then classified by determining the text types of the content. Based on different machine learning algorithms used in the current paper, the system of text classification is divided into four sections namely text pre treatment, text representation, implementation of the classifier and classification. In this paper, a BBC news text classification system is designed. In the classifier implementation section, the authors separately chose and compared logistic regression, random forest and K nearest neighbour as our classification algorithms. Then, these classifiers were tested, analysed and compared with each other and finally got a conclusion. The experimental conclusion shows that BBC news text classification model gets satisfying results on the basis of algorithms tested on the data set. The authors decided to show the comparison based on five parameters namely precision, accuracy, F 1 score, support and confusion matrix. The classifier which gets the highest among all these parameters is termed as the best machine learning algorithm for the BBC news data set.

title: Comparison of Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification
abstract: Today, a largely scalable computing environment provides a possibility of carrying out various data intensive natural language processing and machine learning tasks. One of these is text classification with some issues recently investigated by many data scientists. The authors of this paper investigate Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression classifiers implemented in Apache Spark, i.e. the in memory intensive computing platform. The focus of the paper is on comparing these classifiers by evaluating the classification accuracy, based on the size of training data sets, and the number of n grams. In experiments, short texts for product review data from Amazon were analyzed.

title: Classification of Tweets based on Emotions using Word Embedding and Random Forest Classifiers
abstract: With the large scale penetration of social media into our daily lives, it has become a platform for individuals to share and express their views, feelings, opinions, and thoughts. Identifying emotions has many applications ranging from personalized marketing to behavior study. Individuals express their feelings in a language that is frequently accompanied by ambiguity and figure of speech, which makes it difficult even for humans to comprehend. In this paper, we propose a new approach to classify text into emotion categories. We use Twitter data as labeled input, this data is labeled using hashtags and addresses features like emoticons, emoji, apostrophes, Twitter slang and spelling variations which are a part of informal language on social media. Our model uses word vectors generated by architecture like Word2vec, Glove, and Fasttext to generate word representations of the text. We then investigate the utility of these models on random forest classifier. Ultimately we compare the results to find the best model for text classification based on emotions. We achieve an overall 91% precision for four emotional classes on a mined dataset of more than 100,000 tweets. This is a very useful tool to understand human behavior and a natural step beyond the positive/negative polarity. General Terms Machine Learning, Text Classification, Word Vectors, Natural Language Processing

title: A Semantics Aware Random Forest for Text Classification
abstract: The Random Forest (RF) classifiers are suitable for dealing with the high dimensional noisy data in text classification. An RF model comprises a set of decision trees each of which is trained using random subsets of features. Given an instance, the prediction by the RF is obtained via majority voting of the predictions of all the trees in the forest. However, different test instances would have different values for the features used in the trees and the trees should contribute differently to the predictions. This diverse contribution of the trees is not considered in traditional RFs. Many approaches have been proposed to model the diverse contributions by selecting a subset of trees for each instance. This paper is among these approaches. It proposes a Semantics Aware Random Forest (SARF) classifier. SARF extracts the features used by trees to generate the predictions and selects a subset of the predictions for which the features are relevant to the predicted classes. We evaluated SARF's classification performance on $30$ real world text datasets and assessed its competitiveness with state of the art ensemble selection methods. The results demonstrate the superior performance of the proposed approach in textual information retrieval and initiate a new direction of research to utilise interpretability of classifiers.

title: An improved text classifier based on random forest algorithm comparative studies on multiple text classifiers

title: Sarcasm Detection using Random Forest and Naive Bayes Classifiers
abstract: Sarcasm could be a linguistic development during which individuals state the other of what they really mean. It is a vicinity of opinion mining that is studied so that the various sentiments will be analyzed and worked upon. Sarcasm detection could be a difficult task, even for humans whereas while speaking with each other different body languages are involved due to which it becomes easy to identify sarcasm in the statement. However, just in case of the text information, this body languages are missing thus the sarcasm detection task becomes complicated or difficult. Because the use of social media is inflated the degree of knowledge being generated is growing drastically. With such high volumes of knowledge being generated variety of challenges additionally comes into image. In this work, the text is used to determine the sarcasm. Firstly, the text is preprocessed then the feature choice is employed to mechanically or manually choose those options that contribute most to our prediction variable. Nextly, the classification is completed during which Random Forest, Naive Bayes classification algorithm will be used to classify the text. Primarily, based upon whether or not the statement contains the positive or negative words the sarcasm detection procedure will be carried out.

title: Automatic Text Classification for Label Imputation of Medical Diagnosis Notes Based on Random Forest
abstract: Electronic medical records (EMRs) contain many information of patients, which are of great value for data mining for various clinical applications. However, information missing, including label missing, is pervasive in nature EMRs which would bring lots of obstacles for processing of the medical text contents. The aim of this study is to adopt automatic text classification technologies to recover missing medical text labels for EMRs and support downstream analyses. A combination of word embedding technology and random forest classifiers are applied to identify multiple medical note labels including disease types and examination types, from short texts of medical imaging diagnosis notes. The results show that the average binary classification accuracies are 91% Our research results indicate that using advanced NLP techniques for EMRs can reach high classification accuracies.

title: Stock market random forest text mining system mining critical indicators of stock market movements
abstract: Stock Market (SM) is believed to be a significant sector of a free market economy as it plays a crucial role in the growth of commerce and industry of a country. The increasing importance of SMs and their direct influence on economy were the main reasons for analysing SM movements. The need to determine early warning indicators for SM crisis has been the focus of study by many economists and politicians. Whilst most research into the identification of these critical indicators applied data mining to uncover hidden knowledge, very few attempted to adopt a text mining approach. This paper demonstrates how text mining combined with Random Forest algorithm can offer a novel approach to the extraction of critical indicators, and classification of related news articles. The findings of this study extend the current classification of critical indicators from three to eight classes; it also show that Random Forest can outperform other classifiers and produce high accuracy.

title: Reduced Kernel Random Forest Technique for Fault Detection and Classification in Grid Tied PV Systems
abstract: The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK RF<inline formula><tex math notation="LaTeX"$_{\text{ED}/tex math>/inline formula> and K means clustering based reduced kernel RF (RK RF<inline formula><tex math notation="LaTeX"$_{\text{Kmeans}/tex math>/inline formula> for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid connected PV system is used to validate the performance of the proposed RK RF<inline formula><tex math notation="LaTeX"$_{\text{ED}/tex math>/inline formula> and RK RF<inline formula><tex math notation="LaTeX"$_{\text{Kmeans}/tex math>/inline formula> classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.