Questions tagged [weka]

Weka (Waikato Environment for Knowledge Analysis) is a collection of machine learning algorithms for data mining tasks.

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Weka - random forrest always predicts the same class

I am classifying Portuguese tweets in to three classes, news, noise and relevant. I have used the weka gui to identify a classifying pipeline that gave good results. STWV -> Attribute Selection -> ...
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1answer
300 views

correlationAtrributeEval Weka [closed]

I am new In data mining and Weka. I am working on "Ta_Feng" data set and my Intention is to apply Pearosn's Correlation coefficient to calculate correlation between User and Item attribute in my case ...
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1answer
911 views

Weka PART algorithm output

I am using Weka PART on my data set, and it is providing the rules below: ...
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0answers
94 views

Why random forest performs better when converting categorical attributes to numerical ones?

I have a dataset with 15 attributes, 6 are numerical and 9 are categorical. Among the categorical ones, some have about 3 levels, some about 10 levels and 2 of them about 500 levels. I'm training a ...
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0answers
739 views

Leave- one- subject-out cross validation

I am doing KNN classfication on a dataset composed of 1040 instances. I have 40 subejcts each having 26 samples. I want to do a LOSO validation in WEKA. I divided my data in a way that each time a ...
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0answers
244 views

What is the difference between weka CostSensitiveClassifier and weka MetaCost?

I have read that weka MetaCost's implementation refer to the implementation explained in Pedro Domingo's paper (1999). But I didn't find any explanation of weka CostSensitiveClassifier implementation. ...
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1answer
170 views

ranking neural net models with feature selection

I have a sample with around 2000 observations and 10 variables which im using for classification. I plan on classifying the data with a neural net, but before doing so im using Weka's attribute ...
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1answer
396 views

Prediction of propensity of new customer dataset based on old customer dataset

We have 3 customer data sets. One has customer informations with around 25 attributes like customerID, Gender,Relationship,Job_Level (manager, non-manager), Income level (<25000 USD, between 25000 ...
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3answers
2k views

Cross Validation with Preprocessing (Normalization, Discretization, Feature Selection)

I am now trying to evaluate my model with cross validation. My dataset contains some numeric and nominal attributes. Here, I carry out the following data preprocessing tasks: A. Normalization: Min-...
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1answer
643 views

What does the percentage output represent in RWeka M5P (tree model) output?

I have been teaching myself how to use RWeka, specifically so that I may implement the M5P model. I have been able to use apply to my data, but do not understand what the percentage represents. For ...
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0answers
82 views

Per-instance cost-aware learning?

I have a situation where the misclassification cost depends on the instance, i.e. on the independent variables. In my training set I have for each instance the independent variables plus a vector of ...
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2answers
1k views

Should using SMO classification in WEKA take so long with large dataset?

I have a dataset of 205 features and 238000 samples. It is a combined dataset of several subjects' data that I want to use for between-subject classification. I am using WEKA 3.8 with a 64-bit JVM ...
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0answers
120 views

Obtaining exact output values of SMO based classification, before clearly demarcating them into classes

While running the SMO classifier in weka, if I have inputted my training labels as 0 and 5, (A binary set), then while running the classifier model on test data, are the outputs some decimal values ...
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653 views

How the Correlation Matrix is built for PCA in Weka?

Just to give a context, I want to use PCA (Principal Component Analysis) to identify which attributes are similar to others, so I can use just one (or a subset) of them. The correlation matrix of n ...
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1answer
1k views

Manual and automated calculation of false positive rate in confusion matrix do not agree

If my Weka confusion matrix looks like this: ...
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0answers
113 views

Are Logistic Model Trees robust to imbalanced datasets?

I am trying out Logistic Model Trees (Weka implementation) with very imbalanced datasets. Is anyone aware of their robustness without the need of preprocessing steps (such as SMOTE)? My preliminary ...
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3answers
1k views

Expectation Maximization-Log Likehood interpretation

I am using EM algorithm in weka for genomic data, get the results in the images, but a don't know how interpret the log likehood index. Is better when is higher or lower, negative or positive. How ...
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1answer
975 views

Applying exactly same WEKA filter on train and test data (What to use in setinputFormat traindata or test data) )

I am using WEKA for classification. I need to perform pre-processing before it. I want to do three thing , tf-idf conversion, normalization and discretization. But I want exactly same pre-processing ...
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1answer
286 views

Implementing an Adaboost Classifier

I have generated an adaboost classifier in Weka on a dataset where each instance falls into one of two classes. The result was a number of decision trees, each assigned a weight. What is the ...
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1answer
257 views

What does the number 'Kernel Option' refer to in SVM?

I read that the performance of some kernel functions in SVM can change if we change the number known as kernel option. For example, this article states that kernel option of value 2 was used, http://...
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1answer
3k views

Log likelihood in EM Algorithm [closed]

I try understand the log likelihood in weka. I read about that is a probabilistic metric, but i cant understand, if is better when have low value or high value? How i can get the likelihood value, ...
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0answers
375 views

ensemble methods:voting with average of probabilities in weka

output attribute is risky patient. Values are yes and no.if yes then patient is risky and if no then patient is not risky. If I am combining 3 classifier for classification model in weka, and if ...
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3answers
126 views

Suspicious results after clustering

I've done a clustering and I think that my results are too good to be trusted. Here is my pipeline: Inputs: a dataset of 208 images, distributed into 2 classes (99 and 109 images in each class). ...
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232 views

How to combine random forest from various trained models.

I am currently working on a training data set which is time dependent. I have divided the dataset into different time zones. For each month, I am training the model with training dataset and class ...
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0answers
863 views

How to address Boosting and Bagging decreasing the classification accuracy

For my classification, I use several algorithms available in WEKA, but with limited number of features. I got some accuracy levels with the algorithms I used and I tried improving the accuracies using ...
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1answer
2k views

Accuracy reduced with Adaboost

I tried using AdaBoost for my classification which is for emotion classification. Without boosting, Random Forest algorithm gave me 42.41% of accuracy. But when I applied AdaBoost along with Random ...
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735 views

Accuracy decreased after feature selection

For my machine learning study, I tested different algorithms like SVM, SMO, Naive Bayes, Trees etc. All the algorithms resulted with low accuracy levels. In fact the highest accuracy I obtained was 46%...
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1answer
1k views

Is it wrong if I get training accuracy lower than test accuracy?

I have a dataset with 20000 instances in training, 2300 attributes. I did 10 fold CV and executed on a test set with 9000 instances with naive bayes and J48. The 10 fold CV accuracy is low compared to ...
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1answer
247 views

More details on weka association rules

What are those values right to the itemsets (for example [A=1]:18) If they are absolute support as I thought, why are they different for same items in different rules ? ...
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1answer
5k views

Principal Component Analysis Vs Feature Selection

I am doing a machine learning project using WEKA. It is a supervised classification and in my basic experiments, I achieved very poor level of accuracy. Then my intention was to do a feature selection,...
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1answer
530 views

Manipulating the number of neurons in the hidden layer in Weka [closed]

I'm classifying datasets in Weka with a MLP. My question is how can I change the number of hidden neurons in the hidden layer? Only the option about number of hidden layers is available.
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1answer
2k views

Weka - Run K-Means++ Algorithm in JAVA code to preserve memory

Anyone know how to run weka k-means++ clustering source directly in JAVA code to preserve memory? I load and run k-means++ clustering for large datasets (6 millions) in weka but always freeze, i try ...
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1answer
626 views

Comparing models - accuracy vs. recall

I'm using Weka to generate many models using different functions (Logistic, MultilayerPerceptron, ...
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1answer
386 views

Clustering related areas with k-means in WEKA

I am trying to cluster related areas of knowledge. A sample of my file is: ...
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1answer
3k views

Relation between decision tree Depth and number of Attributes

In Machine Learning libraries such as weka, we can set a tree to be of infinite Depth with maxDepth = -1. I am curious to know what would happen if trees were set to a depth far higher than the number ...
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1answer
568 views

Different result between Weka Java API and R's RandomForest package

I've implemented random forest with both R random forest package and Weka java api. both with same data set (train and test sets are the same) and same configuration (number of trees and mtry). ...
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1answer
753 views

WEKA Experimenter: Statistically significant results appear counterintuitive

In WEKA Experimenter I have a problem that seems counterintuitive and I would like to know whether I correctly interpret the results. My experiment specifics: Type of experiment: Regression Analysis ...
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0answers
52 views

Requirement on data distribution when using certain data mining classifiers

I am a little bit confused on whether data mining classifiers make any assumptions about the distribution of the data. I myself have a computer science background, but when I have discussions with an ...
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2answers
6k views

WEKA: Visualize combined trees of random forest classifier

I have a small data set consisting of 385 entries and around 200 attributes. Because I want to apply attribute selection and because of the limited size of my data set, I got the advice to use the ...
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2answers
600 views

Feature selection when using cross validation

I have a limited size data set of 385 entries on which I want to run multiple classifiers and compare their performance using the WEKA experimenter. The number of attributes in this data set is large,...
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1answer
906 views

Is this training dataset enough for training and testing classification model?

My training dataset contains just 2 classes with 40 features. In case 1, class 1 has 35 samples and class 2 has 700 samples. In case 2, class 1 has 65 samples and class 2 has the same value as ...
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0answers
535 views

WEKA accuracy confidence interval

I have a dataset consisting of thousands of companies from the UK. For 300 of these companies, I collected several indicators which were extracted from social media (e.g. #likes). I have built a model ...
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2answers
12k views

Fatal error using `RWeka::NGramTokenizer` with `tm` to build a term document matrix?

I installed the tm library and want to build n-grams of a corpus using the NGramTokenizer from the ...
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1answer
42 views

How to Filter Junk Features Automatically

A data set that is used to build a regression model might contain "junk" fields. For example if I want to build a model of house prices, the field number of rooms and the size of the house are ...
3
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1answer
5k views

30% difference on accuracy between cross-validation and testing with a test set in weka? is it normal?

I'm new with weka and I have a problem with my text classification project using it. I have a train dataset with 1000 instances and one of 200 for testing. The problem is that when I try to test the ...
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2answers
1k views

How does Weka combine the decision trees in a random forest?

When building the random forest, I am wondering if Weka combine the decision trees by averaging their probabilistic prediction or if Weka let each decision tree vote for a unique class?
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2answers
535 views

Learning a model which can fit the training data accurately

I am using weka for creating a model on a training set for a classification task. I am trying different classifiers for this. But when I try to give one of the data points which are present in the ...
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2answers
582 views

Prediction of n class variables

I have a historical data that has discrete variables. Let say I have data points with class labels 1, 2, 3, 4, and 5 For a given classification problem, I ...
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1answer
52 views

Is this feature redundant?

Say I have a data set, and there's one feature that divides the set into roughly two halves, labeling one half A, and the other half B. Now I have another feature, it labels all instances that were ...
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2answers
1k views

Summary on correctly classfied instances WEKA for a 10-fold cross-validation

I ran a 10-fold cross-validation BayesNet (but it could be any method in WEKA), and the output I got was (among other things): ...