Application of a Rough Set-Based Inductive Learning System

1993 ◽  
Vol 18 (2-4) ◽  
pp. 209-220
Author(s):  
Michael Hadjimichael ◽  
Anita Wasilewska
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Presidential Election ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Inductive Learning ◽  
Real Data ◽  
Semantic Content ◽  
Learning System ◽  
Voter Preferences

We present here an application of Rough Set formalism to Machine Learning. The resulting Inductive Learning algorithm is described, and its application to a set of real data is examined. The data consists of a survey of voter preferences taken during the 1988 presidential election in the U.S.A. Results include an analysis of the predictive accuracy of the generated rules, and an analysis of the semantic content of the rules.

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals Practical Web Spam Lifelong Machine Learning System with Automatic Adjustment to Current Lifecycle Phase

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Marcin Luckner
Keyword(s):  
Machine Learning ◽  
Real Data ◽  
Recognition System ◽  
Learning System ◽  
Machine Learning Techniques ◽  
External Data ◽  
Recognition Quality ◽  
Web Spam ◽  
Learning Set

Machine learning techniques are a standard approach in spam detection. Their quality depends on the quality of the learning set, and when the set is out of date, the quality of classification falls rapidly. The most popular public web spam dataset that can be used to train a spam detector—WEBSPAM-UK2007—is over ten years old. Therefore, there is a place for a lifelong machine learning system that can replace the detectors based on a static learning set. In this paper, we propose a novel web spam recognition system. The system automatically rebuilds the learning set to avoid classification based on outdated data. Using a built-in automatic selection of the active classifier the system very quickly attains productive accuracy despite a limited learning set. Moreover, the system automatically rebuilds the learning set using external data from spam traps and popular web services. A test on real data from Quora, Reddit, and Stack Overflow proved the high recognition quality. Both the obtained average accuracy and the F-measure were 0.98 and 0.96 for semiautomatic and full–automatic mode, respectively.

scholarly journals Amyloid PET-Positive Predictability of Machine Learning Algorithm Based on MDS-OAβ Levels

2021 ◽  
Author(s):  
Young Chul Youn ◽  
Jung-Min Pyun ◽  
Hye Ryoun Kim ◽  
Sungmin Kang ◽  
Nayoung Ryoo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Detection System ◽  
Amyloid Β ◽  
Machine Learning Algorithms ◽  
Amyloid Pet ◽  
Amyloid Positron Emission Tomography ◽  
Positron Emission ◽  
Negative Findings

Abstract Background: The Multimer Detection System-Oligomeric amyloid-β (MDS-OAβ) level is a valuable blood-based biomarker for Alzheimer’s disease (AD). We used machine learning algorithms trained using multi-center datasets to examine whether blood MDS-OAβ values can predict AD-associated changes in the brain.Methods: A logistic regression model using TensorFlow (ver. 2.3.0) was applied to data obtained from 163 participants (amyloid positron emission tomography [PET]-positive and -negative findings in 102 and 61 participants, respectively). Algorithms with various combinations of features (MDS-OAβ levels, age, gender, and anticoagulant type) were tested 50 times on each dataset. Results: The predictive accuracy, sensitivity, and specificity values of blood MDS-OAβ levels for amyloid PET positivity were 78.16±4.97%, 83.87±9.40%, and 70.00±13.13%, respectively.Conclusions: The findings from this multi-center machine learning-based study suggest that MDS-OAβ values may be used to predict amyloid PET-positivity.

scholarly journals Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†

2021 ◽  
Vol 8 (3) ◽  
pp. 209-221
Author(s):  
Li-Li Wei ◽  
Yue-Shuai Pan ◽  
Yan Zhang ◽  
Kai Chen ◽  
Hao-Yu Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Random Forest Regression ◽  
Data Set

Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic Information

2019 ◽  
pp. 141-162
Author(s):  
Magdalena Saldana-Perez ◽  
Miguel Torres-Ruiz ◽  
Marco Moreno-Ibarra
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Life Quality ◽  
Real Data ◽  
Geographic Information ◽  
User Generated Content ◽  
Urban Problem ◽  
Internet Information

Volunteer geographic information and user-generated content represents a source of updated information about what people perceive from their environment. Its analysis generates the opportunity to develop processes to study and solve social problems that affect the people's lives, merging technology and real data. One of the problems in urban areas is the traffic. Every day at big cities people lose time, money, and life quality when they get stuck in traffic jams; another urban problem derived from traffic is air pollution. In the present approach, a traffic event classification methodology is implemented to analyze VGI and internet information related to traffic events with a view to identify the main traffic problems in a city and to visualize the congested roads. The methodology uses different computing tools and algorithms to achieve the goal. To obtain the data, a social media and RSS channels are consulted. The extracted data texts are classified into seven possible traffic events, and geolocalized. In the classification, a machine learning algorithm is applied.

Neuronal Communication Genetic Algorithm-Based Inductive Learning

2021 ◽  
pp. 244-259
Author(s):  
Abdiya Alaoui ◽  
Zakaria Elberrichi
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Predictive Accuracy ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Hybrid Approach ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Neuronal Communication ◽  
Robust Model

The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predictive accuracy and relatively small size. The results on 16 well-known medical datasets from the UCI machine learning repository shows the efficiency of the proposed approach compared to other states-of-the-art approaches.

scholarly journals Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jianwei Liu ◽  
Shuang Cheng Li ◽  
Xionglin Luo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Adaptive Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Support Vector ◽  
Data Set ◽  
Cancer Data ◽  
Public Data

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data.L2regularization has been commonly used. If the training dataset contains many noise variables,L1regularization SVM will provide a better performance. However, bothL1andL2are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweightedp-norm regularization support vector machine for 0 <p≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that apvalue of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using apvalue less thanL1norm. Moreover, we observe that the proposedLppenalty is more robust to noise variables than theL1andL2penalties.

scholarly journals Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Pietro Mascheroni ◽  
Symeon Savvopoulos ◽  
Juan Carlos López Alfonso ◽  
Michael Meyer-Hermann ◽  
Haralampos Hatzikirou
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Tumor Growth ◽  
Clinical Outcomes ◽  
Clinical Presentation ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Mechanistic Modeling ◽  
Lymphocytic Leukemia ◽  
Simulated Patients

Abstract Background In clinical practice, a plethora of medical examinations are conducted to assess the state of a patient’s pathology producing a variety of clinical data. However, investigation of these data faces two major challenges. Firstly, we lack the knowledge of the mechanisms involved in regulating these data variables, and secondly, data collection is sparse in time since it relies on patient’s clinical presentation. The former limits the predictive accuracy of clinical outcomes for any mechanistic model. The latter restrains any machine learning algorithm to accurately infer the corresponding disease dynamics. Methods Here, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning, aiming at improving individualized predictions by addressing the aforementioned challenges. Results We evaluate the proposed method on a synthetic dataset for brain tumor growth and analyze its performance in predicting two relevant clinical outputs. The method results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation (>95% patients show improvements compared to standard mathematical modeling). In addition, we test the methodology in two additional settings dealing with real patient cohorts. In both cases, namely cancer growth in chronic lymphocytic leukemia and ovarian cancer, predictions show excellent agreement with reported clinical outcomes (around 60% reduction of mean squared error). Conclusions We show that the combination of machine learning and mathematical modeling approaches can lead to accurate predictions of clinical outputs in the context of data sparsity and limited knowledge of disease mechanisms.

scholarly journals THE IMPLEMENTATION OF THE MACHINE LEARNING ALGORITHM FOR THE SENTIMENT ANALYSIS OF INDONESIA’S 2019 PRESIDENTIAL ELECTION

2021 ◽  
Vol 22 (1) ◽  
pp. 78-92
Author(s):  
GA Buntoro ◽  
R Arifin ◽  
GN Syaifuddiin ◽  
A Selamat ◽  
O Krejcar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithm ◽  
Presidential Candidates ◽  
N Gram

In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

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