Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method

Abstract

<p>Data mining techniques, extracting patterns from large databases have become widespread in all life’s aspect. One of the most important data mining tasks is classification. Classification is an important and widely studied topic in many disciplines, including statistics, artificial intelligent, operations research, computer science and data mining and knowledge discovery. One of the important things that should be done before using classification algorithms is preprocessing operations which cause to improve the accuracy of classification algorithms. Preprocessing operations include various methods that one of them is normalization. In this paper, we selected five applicable normalization methods and then we normalized selected data sets afterward we calculated the accuracy of classification algorithm before and after normalization. In this study the SVM algorithm was used in classification because this algorithm works based on n-dimension space and if the data sets become normalized the improvement of results will be expected. Eventually Data Envelopment Analysis (DEA) is used for ranking normalization methods. We have used four data sets in order to rank the normalization methods due to increase the accuracy then using DEA and AP-model outrank these methods.</p

Transformation of linguistic values in numerical values using fuzzy logic

“…Pošto su elementi matrice različiti potrebno je izvršiti normalizaciju podataka kako bi se dobila matrica u kojoj svi elementi imaju iste dimenzije. U praksi se najčešće koriste sljedeće normalizacije: vektorska normalizacija, linearna normalizacija tipa 1 (prosta linearna normalizacija), linearna normalizacija tipa 2 (složena linearna normalizacija), linearna normalizacija tipa 3 (postotna normalizacija) i vektorska normalizacija (Eftekhary, et al, 2012). U ovom radu koristit će se linearna normalizacija tipa 1.…”

Learning of Robot Navigation Tasks by Probabilistic Neural Network

“…The Wall-Following Robot Navigation has a very small number of classes, which correspond to four different directions for the robot (Move-Forward, Slight-Right-Turn, Sharp-Right-Turn, Slight-Left-Turn) data set that has 5456 instances with 24 continuous predictor variables and 1 class variable. The predictor variables describe US1: ultrasound sensor at the front of the robot (reference angle: 180°), US2: ultrasound reading (reference angle: 165°), US3: ultrasound reading (reference angle: -150°), US4: ultrasound reading (reference angle: -135°), US5: ultrasound reading (reference angle: -120°), US6: ultrasound reading (reference angle: -105°), US7: ultrasound reading (reference angle: -90°), US8: ultrasound reading (reference angle: -75°), US9: ultrasound reading (reference angle: -60°), US10: ultrasound reading (reference angle: -45°), US11: ultrasound reading (reference angle: -30°), US12: ultrasound reading (reference angle: -15°), US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0°), US14: ultrasound reading (reference angle: 15°), US15: ultrasound reading (reference angle: 30°), US16: ultrasound reading (reference angle: 45°), US17: ultrasound reading (reference angle: 60°), US18: ultrasound reading (reference angle: 75°), US19: ultrasound reading (reference angle: 90°), US20: ultrasound reading (reference angle: 105°), US21: ultrasound reading (reference angle: 120°), US22: ultrasound reading (reference angle: 135°), US23: ultrasound reading (reference angle: 150°), US24: ultrasound reading (reference angle: 165°) [20].The whole dataset consists of about 5456 labelled examples. The actual available moves and class distributions are presented in Table 1.…”

“…In Ref [20], they selected five applicable normalization methods and then they normalized selected data sets afterward they calculated the accuracy of classification algorithm before and after normalization. In study the SVM algorithm was used in classification.…”

A Vikor and Topsis Focused Reanalysis of the Madm Methods Based on Logarithmic Normalization

“…Many theories have been established so as to express transformation through the normalization procedure. Vector normalization, linear normalization, non-monotonic normalization, Weitendorf's linear normalization (WLN) method, the Jüttler-Körth normalization (JKN) method and the Peldschus non-linear normalization (NLN) method [2,7,8,9] are the normalization tools most applied and employed by scholars. Vafaei et al [10] applied six different normalizations methods to evaluate TOPSIS in all the possible ways.…”