International Journal of Scientific & Technology Research

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IJSTR >> Volume 4 - Issue 5, May 2015 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Classification Of Complex UCI Datasets Using Machine Learning And Evolutionary Algorithms

[Full Text]



Anuj Gupta



Index Terms: Classification, Data Mining, Decision Table, Genetic Programming, J48, Logistic, MultilayerPerceptron, NaiveBayes, RandomForest, VFI, ZeroR,



Abstract: Classification is an important data mining technique with broad applications. Classification is a gradual practice for allocating a given piece of input into any of the known category. The Data Mining refers to extracting or mining knowledge from huge volume of data. In this paper different classification techniques of Data Mining are compared using diverse datasets from University of California, Irvine (UCI) Machine Learning Repository. Accuracy and time complexity for execution by each classifier is observed. . Finally different classifiers are also compared with the help of Confusion Matrix. Classification is used to classify each item in a set of data into one of predefined set of classes or groups



[1] Clustering using firefly algorithm: Performance study: J.Senthilnath, S.N. Omkar, V.Mani .

[2] A survey on the Application Of Genetic Programming to Classification: Pedro G. Espejo, Sebastian Ventura, and Francisco Herrera.

[3] Application Of Genetic Programming for Multicategory Pattern Classification : J.K. Kishore, L.M. Patnaik, V.Mani, and V.K. Agarwal.

[4] Black Hole : A new heuristic optimization approach for data clustering.

[5] Comparison of different classification techniques using different datasets: V.vaithiyanathan, K.Rajeswari, Rahul Pitale.

[6] Classification of multivariate datasets without missing values using memory based classifier- An effective evaluation: C.Lakshmi Devasena.

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[8] Applied Evolutionary Algorithms In java: Robert Ghanea Hercock.

[9] Discovering Interesting Classification rules with Genetic Programming: De Falco, A. Della Cioppa, E.Tarantino.

[10] Feature Selection and classification In Genetic Programming : Application to Haptic-Based Biometric data.

[11] Genetic Programming for classificationv learning problems: Thomas loveard.

[12] S.N. Omkar, Manoj kumar M, Dheevatsa Mudigere, Dipti Muley: Urban Satellite Image Classification using Biologically Inspired Techniques

[13] Crop Classification using Biologically-inspired Technique with High Resolution Satellite Image: S. N. Omkar . J. Senthilnath . Dheevatsa Mudigere . M. Manoj Kumar

[14] Crop stage classification of Hyperspectral data using unsupervised techniques: J.Senthilnath, S.N. Omkar, V.Mani, Nitin Karnval and Shreyas P.B .

[15] Hierarchical clustering Algorithm for Land Cover Mapping Using Satellite Images:J.Senthilnath, S.N. Omkar, V.Mani .

[16] Crop Type Classification Based On Clonal Selection Algorithm for High Resolution Sattelite Image: J.Senthilnath, Nitin Karnval, D Sai Teja.

[17] Hierarchical Artificial Immune System For Crop Stage Classification : J. Senthilnath, Nitin Karnval.

[18] Improving the accuracy of Land Use and Land Cover classification of LandSat Data Using Post Classification Enhancement : By Ramita Manandhar and Tiko Ancev.

[19] Satellite Image Processing for land use and land cover Mapping: Ashoka Vanjare, S.N. Omkar, J.Senthilnath.

[20] Classification of Remote Sensing Image Areas Using Feature and Latent Drichlet Allocation Ms Chandrakala, Mrs R. Amsaveni.

[21] Impact of Accuracy, Spatial Availability, and Revisit Time Of Satellite-Derived Surface Soil Moisture in a Multiscale Ensemble Data Assimilation System: Ming Pan and Eric F.Wood.