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Exploring the Machine Learning Algorithms and its Classification

2020 International Journal of Advanced Trends in Computer Science and Engineering  
This manuscript concentrates on describing the perception and evolution of ML, few popular ML algorithms. ML methods might be applied on any broad area.  ...  And provided the success companies see deriving value from massive number of accessible data, everyone wants in.  ...  , firefly algorithm, evolutionary procedures, differential evolution, and so on[32].  ... 
doi:10.30534/ijatcse/2020/264952020 fatcat:y7i6gtctjjcq3b5n5xu3tyjvgm

Querying Biological Sequences Docking Using Different Constraint Programming's: a Survey
English

B.Mallikarjuna Reddy, P Chandrasekhar, M.Ramakrishna Reddy
2015 International Journal of Computer Trends and Technology  
In mixed data extract the useful biological sequences data with subgroup discovery iterative genetic algorithm, Cluster based fuzzy genetic algorithm mining framework, hierarchical fuzzy rule based systems  ...  Machine learning, membership functions, neural networks, artificial intelligence domains has many number of learning methodologies.  ...  Step3: All rules of knowledge features we collect align into hierarchical way. This is two level learning methodology algorithms. Step4: Hierarchical select the features from two rules effectively.  ... 
doi:10.14445/22312803/ijctt-v22p110 fatcat:ag36qn6m4jdkhlakxvrbbisyma

Learning Classifier Systems Approach For Automated Discovery Of Crisp And Fuzzy Hierarchical Production Rules

Suraiya Jabin, Kamal K. Bharadwaj
2007 Zenodo  
This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach.  ...  A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received.  ...  -Unlike most rule induction algorithms LCS do not discover and evaluate rules in isolation.  ... 
doi:10.5281/zenodo.1070514 fatcat:uutc5mpaifhdjdopblbcw6op2y

Survey on Big Data Mining Algorithms

Anushree Raj
2019 International Journal for Research in Applied Science and Engineering Technology  
Technology revolution has been facilitating millions of people by generating tremendous data, resulting in big data.  ...  Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences.  ...  Decision Tree Induction Classification Algorithms In the initial stage different Decision Tree Learning was used to analyze the big data.  ... 
doi:10.22214/ijraset.2019.6234 fatcat:dgcttowerjdkxhu334janvz5z4

Evolutionary learning of hierarchical decision rules

J.S. Aguilar-Ruiz, J.C. Riquelme, M. Toro
2003 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains.  ...  The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must be, therefore, tried in order until one is found whose conditions are satisfied.  ...  The aim of our research was to obtain a set of rules by means of an evolutionary algorithm to classify new examples in the context of supervised learning.  ... 
doi:10.1109/tsmcb.2002.805696 pmid:18238181 fatcat:r4aggn5j7fbhdiq4ldbnejiimm

A Review on Machine Learning Algorithms

Anushree Raj
2019 International Journal for Research in Applied Science and Engineering Technology  
Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy.  ...  The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically.  ...  The goal of unsupervised learning is to discover patterns of regularities and irregularities in a set of observations.  ... 
doi:10.22214/ijraset.2019.6138 fatcat:7flbjojwxvevph6xdvhi37gmzq

Spatial data mining on remote sensing perspective

2016 International Journal of Latest Trends in Engineering and Technology  
Vyas et al. (2005) have experimented with a multi-crop separability study using hierarchical decision rule based supervised classification and could identify a total of 24 classes comprising various land  ...  , and to identify such patterns in images with the help of decision trees.  ... 
doi:10.21172/1.74.036 fatcat:mdrfpf5gefg7lojxpmknvcloym

A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics

Jesmeen M. Z. H, J. Hossen, S. Sayeed, CK Ho, Tawsif K, Armanur Rahman, E.M.H. Arif
2018 Indonesian Journal of Electrical Engineering and Computer Science  
Machine learning algorithms can be used to analyze data and make predictions and finally clean data automatically.</span>  ...  One of the biggest challenges in big data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics and unpredictable conclusions.  ...  outlier factor Machine Learning Supervised learning • Association rule learning algorithms • Gaussian process regression • Instance-based learning • Lazy learningLearning Vector Quantization  ... 
doi:10.11591/ijeecs.v10.i3.pp1234-1243 fatcat:4a6hqthlavcy5disedzsww67ha

Discovering predictive ensembles for transfer learning and meta-learning

Pavel Kordík, Jan Černý, Tomáš Frýda
2017 Machine Learning  
Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.  ...  Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms can be scaled up to model large data sets.  ...  Acknowledgements This research was partially supported by the Modern data-mining methods for advanced extraction of information from data (SG S17/210/O H K 3/3T /18) grant of the Czech Technical University in  ... 
doi:10.1007/s10994-017-5682-0 fatcat:xbxccvy55rbfdliw4prxm4t7cm

Data Mining Methods: A Review

Dimitrios Papakyriakou, Ioannis S. Barbounakis
2022 International Journal of Computer Applications  
A review of learning Density-Based Algorithm for Discovering Clusters in vector quantization classifiers. Neural Comput & Large Spatial Databases with Noise‖. In Proc.  ...  In (SL), datasets are trained with the training set to infer a Unsupervised learning is used for more complex tasks Machine Learning algorithm and then will be used to label compared to supervised  ... 
doi:10.5120/ijca2022921884 fatcat:336vrehj6bgm5dfiqavmhj35mi

Genetics-Based Machine Learning [chapter]

Tim Kovacs
2012 Handbook of Natural Computing  
Introduction Outline This survey: 1 Introduces the subject introduces Supervised Learning (SL) contrasts SL with optimisation assumes readers are familiar with Evolutionary Algorithms (EAs) discusses  ...  Not necessarily more often used a meta-learner for RL Output of multiple runs combined Originated with SIA (Supervised Inductive Algorithm) [299, 200] A supervised genetic rule learner Genetic Cooperative-Competitive  ... 
doi:10.1007/978-3-540-92910-9_30 fatcat:rm5bx5lwdvfalolrky6lpyt67a

GEML: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification [chapter]

Jeannie M. Fitzgerald, R. Muhammad Atif Azad, Conor Ryan
2016 Studies in Computational Intelligence  
The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolution framework  ...  learning algorithms.  ...  Grant number 10/IN.1/I3031.  ... 
doi:10.1007/978-3-319-48506-5_7 fatcat:jz33vloqcbbfdjwmayfcsmwyma

Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution

Asma Gharbi, Cyril De Runz, Sami Faiz, Herman Akdag
2016 Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management  
areas evolution.  ...  In this context, this paper review the main advances in this datamining technique, then attempts to describe how they can, practically, be harnessed to deal with problems such as the prediction of geographical  ...  Class Association rule (CAR) mining algorithms consist in discovering rules in the form of a−→b, where the consequence part (b) have to be an item labelled as a class.  ... 
doi:10.5220/0005914202010206 dblp:conf/gistam/GharbiRFA16 fatcat:gpbigqynojfztgrpndtzdazjj4

Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control

Minrui Fei, S.L. Ho
1999 Proceedings of the IEEE 1999 International Conference on Power Electronics and Drive Systems. PEDS'99 (Cat. No.99TH8475)  
It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms  ...  or other algorithms :should be able to compensate for the disadvantages of neural networks learning.  ...  Based on the correspondence, an efficient hybrid learning strategy, which combines an unsupervised learning algorithm (fuzzy-c-means algorithm) and a supervised learning algorithm (LMS algorithm), was  ... 
doi:10.1109/peds.1999.792863 fatcat:wjwgvmbyzzcadm7d74gqh7onoe

Surveying Human Habit Modeling and Mining Techniques in Smart Spaces

Francesco Leotta, Massimo Mecella, Daniele Sora, Tiziana Catarci
2019 Future Internet  
These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on  ...  A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously  ...  For instance in [35] authors uses them combined with Naive Bayes Classifiers to learn the activity model built on hierarchical taxonomy formalism shown in Figure 3 .  ... 
doi:10.3390/fi11010023 fatcat:hdvhtvf7qbavthdxdwjrwfvjy4
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