Diabetes Prediction using Feature Extraction and Machine Learning Models

2021 ◽  
Author(s):  
Sunil Ghane ◽  
Namrata Bhorade ◽  
Nikita Chitre ◽  
Bhargavi Poyekar ◽  
Rishita Mote ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Models ◽  
Diabetes Prediction ◽  
Machine Learning Models

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

scholarly journals A Machine Learning Model to Identify Duplicate Questions in Social Media Forums

2020 ◽  
Vol 9 (4) ◽  
pp. 370-373
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Extraction ◽  
Good Accuracy ◽  
Learning Model ◽  
Learning Models ◽  
Single Model ◽  
Machine Learning Model ◽  
Repetitive Nature ◽  
Machine Learning Models

In recent years, digital platform forums where question and answers are being discussed are attracting more number of users. Many discussions on these forums would be repetitive nature. Such duplicate questions were provided by Quora as a competition on Kaggle. It is observed that the dataset provided by Quora, requires many modifications before training machine learning models to obtain a good accuracy. These modifications include feature extraction, vectorization and tokenization after which the data is ready for training desired models. While analyzing each model after prediction, it gives plenty of information about its efficiency and many other factors. Later, these information of different models are compared and helps to choose the best model. These models later can be combined and used as a single model with best accuracy. In this paper, a Machine Learning model which will predict duplicate questions is proposed

scholarly journals Electronic Tongue Recognition with Feature Specificity Enhancement

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 772 ◽  
Author(s):  
Tao Liu ◽  
Yanbing Chen ◽  
Dongqi Li ◽  
Tao Yang ◽  
Jianhua Cao
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Extraction Method ◽  
Sensor Array ◽  
Electronic Tongue ◽  
Learning Models ◽  
Learning Methods ◽  
Feature Extraction Method ◽  
Machine Learning Methods ◽  
Machine Learning Models

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

Sign in / Sign up

Export Citation Format

Share Document