Meta-learning Based Optimization of Metabolic Pathway Data-Mining Inference System

2011 ◽  
pp. 183-192 ◽  
Author(s):  
Tomás V. Arredondo ◽  
Wladimir O. Ormazábal ◽  
Diego C. Candel ◽  
Werner Creixell
Keyword(s):  
Data Mining ◽  
Metabolic Pathway ◽  
Inference System ◽  
Pathway Data ◽  
Meta Learning

Bioinformatics Integration Framework for Metabolic Pathway Data-Mining

2006 ◽  
pp. 917-926
Author(s):  
Arredondo V. Tomás ◽  
Seeger P. Michael ◽  
Lioubov Dombrovskaia ◽  
Avarias A. Jorge ◽  
Calderón B. Felipe ◽  
...  
Keyword(s):  
Data Mining ◽  
Metabolic Pathway ◽  
Integration Framework ◽  
Pathway Data

An Integrated Data Mining and Simulation Solution

2010 ◽  
pp. 929-948
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh ◽  
Zaidoun Alzoabi
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Decision Support Tools ◽  
Market Demand ◽  
Inference System ◽  
Car Market ◽  
Neuro Fuzzy ◽  
Support Tools ◽  
Quality In Higher Education

Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education

Pattern Based Feature Construction in Semantic Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 27-65 ◽  
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

scholarly journals reMap: Relabeling Multi-label Pathway Data with Bags to Enhance Predictive Performance

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J. Hallam
Keyword(s):  
Metabolic Pathway ◽  
Software Package ◽  
Performance Metrics ◽  
Predictive Performance ◽  
Prediction Methods ◽  
Sensitivity Score ◽  
Generic Framework ◽  
Pathway Data ◽  
Pathway Prediction ◽  
Low Sensitivity

AbstractWe present reMap (relabeling multi-label pathway data based on bag approach), a simple, and yet, generic framework, that performs relabeling examples to a different set of labels, characterized as bags. A bag is comprised of a subset of correlated pathways, and a pathway is allowed to be mixed over bags, constituting an overlapping pathway over a subset of bags. Bag based approach was followed to overcome low sensitivity scores of triUMPF for the pathway prediction task. The relabeling process in reMap is achieved by alternating between 1) assigning bags to each sample and 2) updating reMap’s parameters. reMap’s effectiveness was evaluated on metabolic pathway prediction where resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved sensitivity score.Availability and implementationThe software package is published on github.com/[email protected]

scholarly journals Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System

2016 ◽  
Vol 2 (2) ◽  
pp. 60
Author(s):  
Abidatul Izzah ◽  
Ratna Widyastuti
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Educational Data Mining ◽  
Fuzzy Rule ◽  
Posttest Score ◽  
Inference System ◽  
Is Implementation ◽  
Attention To Students

AbstrakPerguruan Tinggi merupakan salah satu institusi yang menyimpan data yang sangat informatif jika diolah secara baik. Prediksi kelulusan mahasiswa merupakan kasus di Perguruan Tinggi yang cukup banyak diteliti. Dengan mengetahui prediksi status kelulusan mahasiswa di tengah semester, dosen dapat mengantisipasi atau memberi perhatian khusus pada siswa yang diprediksi tidak lulus. Metode yang digunakan sangat bervariatif termasuk metode Fuzzy Inference System (FIS). Namun dalam implementasinya, proses pembangkitan rule fuzzy sering dilakukan secara random atau berdasarkan pemahaman pakar sehingga tidak merepresentasikan sebaran data. Oleh karena itu, dalam penelitian ini digunakan teknik Decision Tree (DT) untuk membangkitkan rule. Dari uraian tersebut, penelitian bertujuan untuk memprediksi kelulusan mata kuliah menggunakan hybrid FIS dan DT. Data yang digunakan dalam penelitian ini adalah data nilai Posttest, Tugas, Kuis, dan UTS dari 106 mahasiswa Politeknik Kediri pengikut mata kuliah Algoritma dan Struktur Data. Penelitian ini diawali dari membangkitkan 5 rule yang selanjutnya digunakan dalam inferensi. Tahap selanjutnya adalah implementasi FIS dengan tahapan fuzzifikasi, inferensi, dan defuzzifikasi. Hasil yang diperoleh adalah akurasi, sensitivitas, dan spesifisitas  masing-masing adalah 94.33%, 96.55%, dan 84.21%.Kata kunci: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediksi. AbstractCollege is an institution that holds very informative data if it mined properly. Prediction about student’s graduation is a common case that many discussed. Having the predictions of student’s graduation in the middle semester, lecturer will anticipate or give some special attention to students who would be not passed. The method used to prediction is very varied including Fuzzy Inference System (FIS). However, fuzzy rule process is often generated randomly or based on knowledge experts that not represent the data distribution. Therefore, in this study, we used a Decision Tree (DT) technique for generate the rules. So, the research aims to predict courses graduation using hybrid FIS and DT. Dataset used is the posttest score, tasks score, quizzes score, and middle test score from 106 students of the Polytechnic Kediri who took Algorithms and Data Structures. The research started by generating 5 rules by decision tree. The next is implementation of FIS that consist of fuzzification, inference, and defuzzification. The results show that the classifier give a good result in an accuracy, sensitivity, and specificity respectively was 94.33%, 96.55% and 84.21%.Keywords: Decision Tree, Educational Data Mining, Fuzzy Inference System, Prediction.

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