Super Vector Machine(SVM) with Iris and Mushroom Dataset
Upcoming SlideShare
Loading in...5
×
 

Super Vector Machine(SVM) with Iris and Mushroom Dataset

on

  • 968 views

SVM is used to classify the IRIS and Mushroom Dataset.

SVM is used to classify the IRIS and Mushroom Dataset.

Statistics

Views

Total Views
968
Views on SlideShare
968
Embed Views
0

Actions

Likes
0
Downloads
17
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • C-SVC' and 'nu-SVC'. The original SVM formulations for Classification (SVC) used parameter C, [0, inf), to apply a penalty to the optimization for data points which were not correctly separated by the classifying hyperplane
  • It is always better to have k larger as then the training set can pick all the relevant structure
  • I don’t know why there is no change

Super Vector Machine(SVM) with Iris and Mushroom Dataset Super Vector Machine(SVM) with Iris and Mushroom Dataset Presentation Transcript

  • Super Vector Machine with Iris and Mushroom Dataset
  • SVM • In this presentation, we will be learning the characteristics of SVM by analyzing it with 2 different Datasets • 1)IRIS • 2)Mushroom • Both will be implementing on WEKA Data Mining Software
  • What is SVM? • Super Vector Machine or Super Vector Network are supervised Learning Model with associated learning algorithm that analyze data and recognize patterns, used for classification and regression analysis. • The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probablistic binary linear classification -wikipedia
  • IRIS and SVM • IRIS Dataset: The Iris flower data set is a multivariate dataset which quantifies the structural variation of three related species of Iris flower. • Thus classification is done on the basis of flower species which are: • Iris-setosa------------------->Blue • Iris-versicolor -----------------> Red • Iris-verginica ------------------> CYAN colour
  • IRIS and SVM • The data set consists of 50 samples/ instances from each of three species that totals to 150. • Four features were measured from each sample • 1) Sepal Length • 2) Petal Length • 3) Sepal Width • 4) Petal Width • -- all in centimetres. • To distinguish between the species linear discriminant model is used. • Linear discriminant analysis (LDA) are methods used to find a linear combination of features which characterizes or separates two or more classes of objects or events. (wikepedia)
  • IRIS and SVM • So concerning our dataset, as we will be simultaneously analysing the different behaviour of the four features as mentioned above for the three different species of the Iris flower. • In IRIS, we will be implementing multi-class SVM model, as there are more than 3 classes. • We can see from the below image that class 'Iris setosa' is linearly separable and other two classes are not. Thus dataset like Iris is linearly not separable which could be a best example to implement SVM.
  • Implementation of SVM • The multi-class SVM will be implemented by LIBSVM library. LIBSVM implements the SMO algorithm for kernelized support vector machines(SVMs), supporting classification and regression. LIBSVM implement one against one strategy for multiclass implementation. LIBSVM to build SVM classes • The one against one strategy, also known as “pairwise coupling”, “all pairs” or “round robin”, consists in constructing one SVM for each pair of classes. Thus, for a problem with c classes, c(c-1)/2 SVMs are trained to distinguish the samples of one class from the samples of another class. Usually, classification of an unknown pattern is done according to the maximum voting , where each SVM votes for one class. [http://hal.archivesouvertes.fr/docs/00/10/39/55/PDF/cr102875872670.pdf pp.4]
  • General Classification of IRIS • Its shown in the histogram that how different feature of each training example i.e measurements of petal and sepal width and length, classify each example into different classes. The below classification is on the basis of sepal length
  • Classification-SVM algorithms • To construct an optimal hyperplane, SVM employs an iterative training algorithm, which is used to minimize an error function. According to the form of the error function, SVM models can be classified into four distinct groups: • Classification SVM Type 1 (also known as C-SVM classification) • Classification SVM Type 2 (also known as nu-SVM classification) • [https://www.statsoft.com/textbook/support-vectormachines]
  • Testing both algorithms, it was found that C-SVM have better performance over nu-SVM . The MSE and RSE in C-SVM was found as 0.22 and 0.149, whereas the same in nu-SVM was measured as 0.26 and 0.16
  • Kernal Type. As it is on Multi-classes dataset thus it will be using the kernel trick. There are four kernel functions available for selection
  • SVM Kernels • Radial basis kernel function is most popular and most widely used from all. Different Kernel Functions will generate different confusion matrix • In general, the RBF kernel is a reasonable first choice. This kernel nonlinearly maps samples into a higher dimensional space so it, unlike the linear kernel, can handle the case when the relation between class labels and attributes is nonlinear
  • SVM Kernels • With Radial Basis • With Polynomial Kernel
  • • Using same training set for test set • Using different test set from the original training set • Cross validation method • Percentage Split. if 10% then it means 10% training data and 90% test data Testing Iris Dataset via SVM
  • Cross Validation Technique Results with 10-Fold Results with 15-Folds
  • Percentage Split Test Set 50% 70%
  • ROC Curve for Iris-Setosa
  • ROC Curve for Iris- Versicolor
  • ROC Curve for Iris-Virginica
  • MUSHROOM DATASET • This dataset is a sample of 23 different species of mushroom, which has the poisonous and edible effect. Thus, the training set will categorize each species in to 2 classes.. Thus it will train the future mushroom samples to fall into either of two categories depends upon its similarity with the other 23 species. • Total instances we have 8124 • In the following picture, Edible is shown in Blue Poisonous is in Red
  • Mushroom and SVM Following example will show how one of the feature of mushroom when have certain effect out of 9 categories, will classify it into Edible or Poisonous. Like if it smells Fishy i.e 'f' which have a count of 2160 has more probability of being poisonous.
  • Implementation of SVM • In this dataset SVM model is used as binary classifier(default) doing linear classification. • It is implemented by Weka’s default algorithm SMO(Sequential Minimal optimization), which is also used in LibSVM • This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes by default. • Linear Binary kernel used k<x,y>=x,y • As like LibSVM it has different kernel functions. By default it uses PolyKernel pulls out the following result. I did try to implement other kernels but it was too slow to process 8124 instances
  • As like LibSVM it has different kernel functions. By default it uses PolyKernel that pulls out the following result. I did try to implement other kernels but it was too slow to process 8124 instances
  • Cross Validation Technique Results with 10-Fold Results with 90-Folds
  • Percentage Split Test Set 50% 70%
  • ROC for Edible Mushroom
  • ROC for Poisonous Mushroom