May2020 International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) by Editor IJITCE

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020

Dear Researcher, Greetings! Articles in this issue discusses about Comparative Study on the role of dimensionality in Bigdata. It has been an absolute pleasure to present you articles that you wish to read. We look forward many more new technologies in the next month.

Thanks, Editorial Team IJITCE

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020

Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA. Dr. S.Prasath Ph.D Assistant Professor, Department of Computer Science, Nandha Arts & Science College, Erode , Tamil Nadu, India

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Review Board Members Mr. Rajaram Venkataraman Chief Executive Officer, Vel Tech TBI || Convener, FICCI TN State Technology Panel || Founder, Navya Insights || President, SPIN Chennai Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE

Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020

Contents Comparative Study on the role of dimensionality in Bigdata

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020

Comparative Study on the role of dimensionality in Bigdata Pradeepan P#1, Gladston Raj S#2 #1

Varavilathoppu poovar, Trivandrum, Kerala, India 1

Pradeep0076@gmail.com

Associate Professor & Head of Computer Science, Government College Nedumangad, Trivandrum, Kerala, India 2

gladston@rediffmail.com Abstract— The curse of dimensionality refers to all the to various challenges that arise when analysing data in problems that arise when working with data in the higher high-dimensional spaces that do not occur in lowdimensions that did not exist in the lower dimensions. As dimensional settings. High dimensionality coupled with the number of features increase, the number of samples large sample sizes creates issues such as heavy also increases proportionally. The more features we have, computational cost and algorithmic instability. the more number of samples we need to have all combinations of feature values well represented in our sample. As the number of features increases, the machine II. WHY REDUCE DIMENSIONALITY learning model becomes more complex. The more the number of features, the more the chances of over fitting. A We need a huge number of data points to get an accurate machine learning model that is trained on a large number prediction from a machine learning model in the cases of high of features, gets increasingly dependent on the data, it dimensional data. The number of data points required for the was trained on and in turn over fitted, leading to poor model to perform well increases exponentially with an increase performance on real data, beating the purpose avoiding in dimensionality. It is not feasible in some situations to train over fitting is a major motivation for performing the model with a very large data. Main characteristic of dimensionality reduction. The paper presents a review and dimensionally reduced data listed below systematic comparison of different dimensionality • dataset will be less complex reduction techniques, also explains the strength and • dataset will take up small storage space weakness of these techniques. • dataset will require less computation time Keywords: big data, machine learning, dimensionality • dataset will have lower chance of model over fitting

reduction I. INTRODUCTION We now live in an era of data deluge where large volumes of data are accumulating in all aspects of our lives. Data streams coming from diverse domains contribute to the emerging paradigm of big data. It may be a great opportunity for the data scientist amongst the vast amount and array of data. By discovering associations, analyzing patterns and predicting trends within the data, big data has the potential to change our society and improve the quality of our life. Big data refers to the following three types based on data sources from physical, cyber, and social worlds. While big data brings great opportunities, unpredictable challenges are on the way at the same time. It cannot be stored, analyzed and processed by traditional data management technologies and requires adaptation of some new workflows, platforms and architectures. The field of machine learning which is useful to accomplish tasks of prediction, classification, and association about large amounts of data, is getting more and more attention from researchers in the current time. However, as the big data era is coming, some characteristics of bigdata will bring great challenges to the traditional machine learning methods. Volume is the important aspect of big data. As data volumes and varieties grow, processing and consuming the insights generated becomes challenging. The curse of dimensionality refers

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III. TECHNIQUES FOR DIMENSIONALITY REDUCTION There are many techniques that can be used for dimensionality reduction. In this section, we review some main techniques. A. Feature Selection Feature selection is for remove the irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates extracted ones. B. Feature Extraction Feature extraction is for creating a new, smaller set of features that still extract most of the useful information. Fig 1: (a) Feature selection methods

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 Fig 1 :( b) Feature extraction methods

1) High Correlation filter: High correlation between two variables means they have same trends and maybe carry similar information. This can bring down the performance of machine learning models drastically (linear and logistic regression models, for instance). We can calculate the correlation between independent numerical variables that are numerical in nature. If the correlation coefficient crosses a certain threshold, we can drop one of the variables (dropping a variable is highly subjective and should always be done keeping the domain in mind). We use Pearson correlation coefficient to find out the correlation coefficient. Strengths: •

Applying correlation thresholds is also based on solid intuition: similar features provide redundant information. Some algorithms are not robust to correlated features, so removing the correlated features that can boost performance

2) Random Forests/Ensemble Trees: Random forest is a supervised ensemble tree based machine learning algorithm that is used for both classifications as well as regression problems. But however, it is mainly used for classification problems. As we know that a forest is made up of trees and more trees mean more robust forest. Similarly, the random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally chooses the best solution by means of voting. It is an ensemble method that is provide better solution than a single decision tree because it reduces the over-fitting by averaging the result. Decision tree ensembles useful for column selection in addition to being effective classifiers. If we generate a large and carefully constructed set of trees to predict the target classes and then use each column’s usage statistics to find the most informative subset of columns. If a column is often selected as the best split, it is very likely to be an informative column that we must keep. For all columns, we calculate a scorecard as the number of times that the column was selected for the split, divided by the number of times in which it was a candidate. The most predictive columns are those which have the highest scores. Fig 2: simplified random forest

Pearson’s correlation coefficient equation

Random forest creation 1. Randomly select “k” features from total “m” features where k << m 2. Among the “k” features, calculate the node “d” using the best split point 3. Split the node the best split

Weaknesses:

into daughter

nodes using

•

We must manually set a correlation threshold, which can be tricky to do.

4. Repeat the 1 to 3 steps until “l” number of nodes has been reached

•

If you set your threshold too low, there is a risk for dropping useful information. Whenever possible, prefer algorithms with built-in feature selection over correlation thresholds. Even for algorithms without built-in feature selection, Principal Component Analysis (PCA) is often provide a better solution.

5. Build forest by repeating steps 1 to 4 for “n” number times to create “n” number of trees.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 Strengths: •

Decision trees can learn non-linear relationships, and are fairly robust to outliers. Ensembles perform very well in practice.

(b): Reduced data from 3D to 2D

Weaknesses: •

Unconstrained, individual trees have tendency to over fitting because they can keep branching until they memorize the training data. However, this can be alleviated by using ensembles Strengths:

3) Principal Component Analysis:

•

Principal component analysis (PCA) is an unsupervised technique and it is a statistical procedure that transforms the original n numeric dimensions of a dataset into a new set of n dimensions called principal components. As a result of the transformation, the first principal component has the largest possible variance. After each succeeding principal component has the highest possible variance under the constraint that it is orthogonal to the preceding principal components. Keeping only the first m < n (A number m of linear combinations of the n input features) principal components reduces the data dimensionality while retaining most of the data information, i.e., variation in the data. Notice that the PCA transformation is sensitive to the relative scaling of the original columns, and therefore, the data need to be normalized before applying PCA. Also notice that the new components are not real, system-produced variables anymore. Applying PCA to your dataset loses its interpretability. If interpretability of the results is important for your analysis, PCA is not the transformation that you should apply. The principal component’s (PC’s) possess some useful properties which are listed below: 1. The PCs are essentially the linear combinations of the original variables, the weights vector in this combination is actually the eigenvector found which in turn satisfies the principle of least squares. 2. The PCs are orthogonal 3. The variation present in the PCs decrease as we move from the 1st PC to the last one, hence the importance. Fig 3 (a): Reduced data from 2D to 1D

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PCA is a versatile technique that works well in practice. It's fast and simple to implement, which means you can easily implement the algorithms with and without PCA to compare performance. In addition, PCA offers several variations and extensions (i.e. kernel PCA, sparse PCA, logistic PCA etc.) to tackle specific roadblocks.

Weaknesses: •

The new principal components are not interpretable, which may be a deal-breaker in some settings. In addition, you must still manually set a threshold for cumulative explained variance.

•

Only for numeric columns.

4) Linear Discriminant Analysis (LDA): Linear Discriminant Analysis seeks to best separate (or discriminate) the samples in the training dataset by their class value. Specifically, the model seeks to find a linear combination of input variables that achieves the maximum separation for samples between classes (class centroids or means) and the minimum separation of samples within each class. A number m of linear combinations (discriminant functions) of the n input features, with m < n, are produced to be uncorrelated and to maximize class separation. These discriminant functions become the new basis for the dataset. All numeric columns in the dataset are projected onto these linear discriminant functions, effectively moving the dataset from the n-dimensionality to the mdimensionality. In order to apply the linear discriminant analysis technique for dimensionality reduction, the target column has to be selected first. The maximum number of reduced dimensions (m) is the number of classes in the target column minus one, or if smaller, the number of numeric columns in the data. Notice that linear discriminant analysis assumes that the target classes follow a multivariate normal distribution with the same variance but with a different mean for each class.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 Strengths: LDA is supervised, which can (but doesn't always) improve the predictive performance of the extracted features. Furthermore, LDA offers variations (i.e. quadratic LDA) to tackle specific roadblocks. Fig 4 (a) Before LDA

(b) After LDA

V. RESULTS & DISCUSSION The experimentation is performed on customer satisfaction dataset which using Python 3 in Pentium(R) dual-core processor with 2GB ram.

Weaknesses: •

•

experimented using the aforementioned ML algorithm. The results obtained are again evaluated. 5) In step-5 LDA is applied on the filtered dataset. The resultant dimensionally reduced dataset is then experimented using the ML algorithm. The results obtained are again evaluated. 6) In step-6 analyzed and compared the accuracy and computational time of original dataset and after applied the above dimensionality reduction techniques through Random forest classification, then calculated the computational time reduction rate.

As with PCA, the new features are not easily interpretable, and you must still manually set or tune the number of components to keep. LDA also requires labeled data, which makes it more situational. Only for numeric columns requires normalization and a target column to separate the data.

IV. METHODOLOGY To test how effective various dimensionality reduction algorithms are at projecting data from high dimensional to low dimension took ‘customer satisfaction’ dataset from kaggle public data platform and partitioned this dataset into a training set and a testing set. For this review opted 10000 rows and 370 columns. The partition is done as follow: First n rows are selected for test the remaining rows (10000-n) are used for training set and for computing the accuracy and time reduction rate for the above mentioned dimensionality reduction techniques. The various steps used in this work are discussed below: 1) In step-1 feature selection technique filter method used in the dataset for removing constant, quasiconstant and duplicate elements on the customer satisfaction dataset. 2) In step-2, the dataset is experimented using tree based machine learning algorithm Random Forest. The accuracy and computational time of this classifier is calculated. 3) In step-3, highest correlation filter applied on the filtered dataset to extract the most prominent features. The resultant dataset is then experimented on the Random forest algorithm. The ML algorithm using Highest correlation filter is evaluated, the accuracy and computational time. 4) In the step-4, PCA is applied on the filtered dataset. The resultant dimensionally reduced dataset is then

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Table 1: accuracy and process time measured in this methodology ORIGINAL DATA

HCF

LDA

PCA

ACCURACY

95.8

95.85

92.8

96.1

PROCESS TIME (Sec)

3.14

1.5

1.33

0.546

The figures describe the accuracy and computational time reduction rate of the above specified techniques when applied them to the database, fig 5(a) shows that the accuracy rate not moving an excessive amount of mentioned dimensionality reduction techniques, principal components analysis provide better accuracy than the original data, high correlation filter provide similar to the original data and linear discriminant analysis provide less accuracy than the original data.

Fig 5(a) : accuracy comparison of original data and dimensionally reduced data.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.10 NO 5 MAY 2020 5 (b): computational time reduction rate of highest correlation filter, principal component analysis and linear discriminant analysis after dimensionality reduction

Within the case of time reduction rate, its make huge impact in the computation time, the fig 5(b) shows that after using dimensionality reduction techniques we can reduce computational time effectively, while not moving the accuracy of the database. VI. CONCLUSION To address visualization challenges posed by big and high dimensional data, this algorithms and techniques that compress the amount of data and/or reduce the number of attributes to be analyzed and visualized. By combining these approaches several benefits result. First, the storage space required for computing inmemory visualization is reduced and rendering speeds increase. Next, computational resources and consumption-based cloud data costs are decreased, as there is less data to process. Also, model error and accuracy are improved. Finally, it becomes easier to visualize patterns and trends more clearly, allowing key insights to be generated. There is no best technique for dimensionality reduction and no mapping of techniques to problems. Each technique has its own merits and demerits; instead the best approach is to use systematic controlled experiments to discover what dimensionality reduction technique when paired with your model of choice, result in the best performance on your dataset.

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