An Extensive Text Mining Study for the Turkish Language

2021 ◽  
pp. 272-306
Author(s):  
Durmuş Özkan Şahin ◽  
Erdal Kılıç
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Text Mining ◽  
Language Processing ◽  
Information Gain ◽  
Learning Algorithms ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Chi Square

In this study, the authors give both theoretical and experimental information about text mining, which is one of the natural language processing topics. Three different text mining problems such as news classification, sentiment analysis, and author recognition are discussed for Turkish. They aim to reduce the running time and increase the performance of machine learning algorithms. Four different machine learning algorithms and two different feature selection metrics are used to solve these text classification problems. Classification algorithms are random forest (RF), logistic regression (LR), naive bayes (NB), and sequential minimal optimization (SMO). Chi-square and information gain metrics are used as the feature selection method. The highest classification performance achieved in this study is 0.895 according to the F-measure metric. This result is obtained by using the SMO classifier and information gain metric for news classification. This study is important in terms of comparing the performances of classification algorithms and feature selection methods.

AUTOMATED SELECTION OF INPUTS FOR LOG PREDICTION MODELS USING A NEW FEATURE SELECTION METHOD

2021 ◽  
Author(s):  
Ravi Arkalgud ◽  
◽  
Andrew McDonald ◽  
Ross Brackenridge ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Target Feature ◽  
Geomechanical Properties ◽  
New Feature ◽  
Selection Of

Automation is becoming an integral part of our daily lives as technology and techniques rapidly develop. Many automation workflows are now routinely being applied within the geoscience domain. The basic structure of automation and its success of modelling fundamentally hinges on the appropriate choice of parameters and speed of processing. The entire process demands that the data being fed into any machine learning model is essentially of good quality. The technological advances in well logging technology over decades have enabled the collection of vast amounts of data across wells and fields. This poses a major issue in automating petrophysical workflows. It necessitates to ensure that, the data being fed is appropriate and fit for purpose. The selection of features (logging curves) and parameters for machine learning algorithms has therefore become a topic at the forefront of related research. Inappropriate feature selections can lead erroneous results, reduced precision and have proved to be computationally expensive. Experienced Eye (EE) is a novel methodology, derived from Domain Transfer Analysis (DTA), which seeks to identify and elicit the optimum input curves for modelling. During the EE solution process, relationships between the input variables and target variables are developed, based on characteristics and attributes of the inputs instead of statistical averages. The relationships so developed between variables can then be ranked appropriately and selected for modelling process. This paper focuses on three distinct petrophysical data scenarios where inputs are ranked prior to modelling: prediction of continuous permeability from discrete core measurements, porosity from multiple logging measurements and finally the prediction of key geomechanical properties. Each input curve is ranked against a target feature. For each case study, the best ranked features were carried forward to the modelling stage, and the results are validated alongside conventional interpretation methods. Ranked features were also compared between different machine learning algorithms: DTA, Neural Networks and Multiple Linear Regression. Results are compared with the available data for various case studies. The use of the new feature selection has been proven to improve accuracy and precision of prediction results from multiple modelling algorithms.

scholarly journals Integrating Feature Selection and Multiclass Classification for Sport Result Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 1339-1342
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Learning Models ◽  
Sports Fans ◽  
Player Performance ◽  
Machine Learning Models

Machine learning (ML) has become the most predominant methodology that shows good results in the classification and prediction domains. Predictive systems are being employed to predict events and its results in almost every walk of life. The field of prediction in sports is gaining importance as there is a huge community of betters and sports fans. Moreover team owners and club managers are struggling for Machine learning models that could be used for formulating strategies to win matches. Numerous factors such as results of previous matches, indicators of player performance and opponent information are required to build these models. This paper provides an analysis of such key models focusing on application of machine learning algorithms to sport result prediction. The results obtained helped us to elucidate the best combination of feature selection and classification algorithms that render maximum accuracy in sport result prediction.

An analysis of PCOS disease prediction model using machine learning classification algorithms

2020 ◽  
Vol 15 ◽  
Author(s):  
Shivani Aggarwal ◽  
Kavita Pandey
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Metabolic Abnormalities ◽  
Related Disorder ◽  
Machine Learning Classification

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women in reproductive age. PCOS is the most usually occurring hormone-related disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came into light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorder may occur which increase the risk of insulin resistance, type 2 diabetes and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at higher hazardous level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) are uses well organized test datasets for classify huge records. Initially a dataset of 541 instances and 41 attributes has been taken to apply the prediction models and a manual feature selection is done over it. Results: After the feature selection, a set of 12 attributes has been identified which plays a crucial role in diagnosing PCOS. Conclusion: There are several researches progressing in the direction of diagnosing PCOS but till now the relevant features are not identify for the same.

scholarly journals Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

2019 ◽  
Vol 20 (9) ◽  
pp. 2185 ◽  
Author(s):  
Xiaoyong Pan ◽  
Lei Chen ◽  
Kai-Yan Feng ◽  
Xiao-Hua Hu ◽  
Yu-Hang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Expression Pattern ◽  
Learning Algorithms ◽  
Expression Patterns ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Feature List ◽  
Cancer Types

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Efficient Approach of Automatic Speech Emotion Recognition (ASR) Using Mutual Information

2021 ◽  
Vol 9 (1) ◽  
pp. 595-603
Author(s):  
Shivangi Srivastav, Rajiv Ranjan Tewari
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Mutual Information ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Learning Algorithms ◽  
Feature Selection Method ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Speech Emotion Recognition

Speech is a significant quality for distinguishing a person in daily human to human interaction/ communication. Like other biometric measures, such as face, iris and fingerprints, voice can therefore be used as a biometric measure for perceiving or identifying the person. Speaker recognition is almost the same as a kind of voice recognition in which the speaker is identified from the expression instead of the message. Automatic Speaker Recognition (ASR) is the way to identify people who rely on highlights that are omitted from speech expressions. Speech signals are awesome correspondence media that constantly pass on rich and useful knowledge, such as a speaker's feeling, sexual orientation, complement, and other interesting attributes. In any speaker identification, the essential task is to delete helpful highlights and allow for significant examples of speaker models. Hypothetical description, organization of the full state of feeling and the modalities of articulation of feeling are added. A SER framework is developed to conduct this investigation, in view of different classifiers and different techniques for extracting highlights. In this work various machine learning algorithms are investigated to identify decision boundary in feature space of audio signals. Moreover novelty of this art lies in improving the performance of classical machine learning algorithms using information theory based feature selection methods. The higher accuracy retrieved is 96 percent using Random forest algorithm incorporated with Joint Mutual information feature selection method.

scholarly journals The pertinent single-attribute-based classifier for small datasets classification

2020 ◽  
Vol 10 (3) ◽  
pp. 3227
Author(s):  
Mona Jamjoom
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Information Gain ◽  
Selection Criterion ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Benchmark Datasets ◽  
Single Attribute ◽  
Ratio Measure

Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In this paper, we revealed the power of a single attribute by introducing the pertinent single-attribute-based-heterogeneity-ratio classifier (SAB-HR) that used a pertinent attribute to classify small datasets. The SAB-HR’s used feature selection method, which used the Heterogeneity-Ratio (H-Ratio) measure to identify the most homogeneous attribute among the other attributes in the set. Our empirical results on 12 benchmark datasets from a UCI machine learning repository showed that the SAB-HR classifier significantly outperformed the classical OneR classifier for small datasets. In addition, using the H-Ratio as a feature selection criterion for selecting the single attribute was more effectual than other traditional criteria, such as Information Gain (IG) and Gain Ratio (GR).

Phishing web site detection using diverse machine learning algorithms

2020 ◽  
Vol 38 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Ammara Zamir ◽  
Hikmat Ullah Khan ◽  
Tassawar Iqbal ◽  
Nazish Yousaf ◽  
Farah Aslam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Information Gain ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Data Set ◽  
Content Type

Purpose This paper aims to present a framework to detect phishing websites using stacking model. Phishing is a type of fraud to access users’ credentials. The attackers access users’ personal and sensitive information for monetary purposes. Phishing affects diverse fields, such as e-commerce, online business, banking and digital marketing, and is ordinarily carried out by sending spam emails and developing identical websites resembling the original websites. As people surf the targeted website, the phishers hijack their personal information. Design/methodology/approach Features of phishing data set are analysed by using feature selection techniques including information gain, gain ratio, Relief-F and recursive feature elimination (RFE) for feature selection. Two features are proposed combining the strongest and weakest attributes. Principal component analysis with diverse machine learning algorithms including (random forest [RF], neural network [NN], bagging, support vector machine, Naïve Bayes and k-nearest neighbour) is applied on proposed and remaining features. Afterwards, two stacking models: Stacking1 (RF + NN + Bagging) and Stacking2 (kNN + RF + Bagging) are applied by combining highest scoring classifiers to improve the classification accuracy. Findings The proposed features played an important role in improving the accuracy of all the classifiers. The results show that RFE plays an important role to remove the least important feature from the data set. Furthermore, Stacking1 (RF + NN + Bagging) outperformed all other classifiers in terms of classification accuracy to detect phishing website with 97.4% accuracy. Originality/value This research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.

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