Noise in Datasets: What Are the Impacts on Classiﬁcation Performance?

Rashida Hasan

and Cheehung Henry Chu

Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, U.S.A.

Keywords:

Machine Learning, Classiﬁers, Learning From Noisy Data, Class Noise, Attribute Noise.

Abstract:

Classiﬁcation is one of the fundamental tasks in machine learning. The quality of data is important in con-

structing any machine learning model with good prediction performance. Real-world data often suffer from

noise which is usually referred to as errors, irregularities, and corruptions in a dataset. However, we have no

control over the quality of data used in classiﬁcation tasks. The presence of noise in a dataset poses three major

negative consequences, viz. (i) a decrease in the classiﬁcation accuracy (ii) an increase in the complexity of

the induced classiﬁer (iii) an increase in the training time. Therefore, it is important to systematically explore

the effects of noise in classiﬁcation performance. Even though there have been published studies on the effect

of noise either for some particular learner or for some particular noise type, there is a lack of study where the

impact of different noise on different learners has been investigated. In this work, we focus on both scenar-

ios: various learners and various noise types and provide a detailed analysis of their effects on the prediction

performance. We use ﬁve different classiﬁers (J48, Naive Bayes, Support Vector Machine, k-Nearest Neigh-

bor, Random Forest) and 10 benchmark datasets from the UCI machine learning repository and three publicly

available image datasets. Our results can be used to guide the development of noise handling mechanisms.

1 INTRODUCTION

In machine learning, classiﬁcation is a supervised

learning approach in which the model learns from the

data given to it and makes new observations or pre-

dicts the class. The maximum prediction accuracy of

a classiﬁer depends on two factors (i) quality of the

training data and (ii) the inductive bias of the algo-

rithm(Zhu and Wu, 2004). But real-world datasets are

not perfect and may suffer from noise. It is referred

to as meaningless, erroneous, or corrupted data in a

dataset.

In the data collection process, issues such as mea-

surement errors, incomplete, corrupted, wrong, or

distorted examples may be introduced (Libralon et al.,

2009). This may result in errors in the values of the

attributes or the class label(Nettleton et al., 2010).

Noisy data may bias the learning process and make

it difﬁcult for the learner to build accurate models.

Over the last few years, many algorithms have been

developed to learn from noisy environments (Nazari

et al., 2018). But the existence of noise can still in-

troduce negative impacts. Therefore, it is important

to develop some data preprocessing mechanisms that

https://orcid.org/0000-0002-6231-8116

https://orcid.org/0000-0002-5817-8798

can effectively and efﬁciently deal with these types

of data. In order to deal with real-world datasets,

the algorithm requires an existing preprocessing mod-

ule that will determine the impact of noise. Unfortu-

nately, very few works have been conducted to inves-

tigate the impact of noise. This work investigates the

impact of various noise types on different classiﬁers.

Our aims are to extract information about the effect

of different types and degrees of noise on these clas-

siﬁers by systematically evaluating the effects of dif-

ferent types and degrees of noise in different learning

paradigms. Our study investigates (i) is the perfor-

mance of a classiﬁer is hampered by noise? (ii) what

is the impact of noise on the classiﬁer if the training

dataset is large enough? (iii) is there any robust clas-

siﬁer for noisy environments? (iv) class noise vs at-

tribute noise: which one is more detrimental to the

classiﬁcation performance?

We aim at studying the performance of ﬁve classi-

ﬁers including J48, Naive-Bayes (NB), Support Vec-

tor Machine (SVM), k Nearest Neighbor (k-NN), and

Random Forest (RF). In our experiments, we em-

ploy linear classiﬁers as well as non-linear classiﬁers.

Firstly, random class noise with different degrees is

analyzed. Then we analyze the impact of attribute

noise. Our work also aims to ﬁnd a robust classiﬁer

Hasan, R. and Chu, C.

Noise in Datasets: What Are the Impacts on Classiﬁcation Performance?.

DOI: 10.5220/0010782200003122

In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 163-170

ISBN: 978-989-758-549-4; ISSN: 2184-4313

163

that is less sensitive to noise. One of the key attrac-

tions is that we take into account different characteris-

tics of the dataset to provide a broader aspect of noise

impacts. The results enable us to highlight the strong

and weak classiﬁers in the presence of noise.

The rest of the paper is organized as follows. Sec-

tion 2 discusses the related work. In Section 3, we

differentiate between class and attribute noise as well

as present our noise injection methodology. Section 4

reports the experimental results. Finally, we conclude

the paper in Section 5.

2 RELATED WORK

Our work as described in Section 1 is motivated by

the observation that while previous work have fo-

cused on the effect of class noise (Zhu and Wu, 2004),

(Nazari et al., 2018), (Pelletier et al., 2017), (Al-

gan and Ulusoy, 2020), there is limited attention to

the impact of attribute noise in a dataset. The main

limitation of some of these approaches is that they

conducted experiments only for speciﬁc applications

such as land cover mapping dataset (Nazari et al.,

2018) or image dataset(Algan and Ulusoy, 2020). In

the few studies that include different datasets, they

consider only attribute noise (Nettleton et al., 2010),

(da Costa et al., 2016), (Rolnick et al., 2017), (Saseen-

dran et al., 2019). Little has been done to measure

the impact of both types of noise such as attribute

noise and class noise. Few works investigated the im-

pact of both types of noises but for a speciﬁc learner

(Zhu and Wu, 2004). The comparisons of the effect

of noise on different learning paradigms have been

neglected (Nazari et al., 2018), (Algan and Ulusoy,

2020), (da Costa et al., 2016), (Saseendran et al.,

2019).

Few studies have reported the sensitivity of ma-

chine learning algorithms against noise. Most exist-

ing algorithms aim to learn directly from noisy data

(Rolnick et al., 2017). Zhu and Wu (Zhu and Wu,

2004) showed that with an increase in the attribute

noise, the accuracy of the classiﬁer decreases linearly.

They also demonstrated that eliminating instances

containing class noise will likely enhance classiﬁca-

tion performance. However, their experiments were

limited to a speciﬁc learner such as C4.5. Nettleton et

al (Nettleton et al., 2010) also pointed out the impact

of class noise and attribute noise. Their experimental

results suggest that NB was more robust to noisy data

and SVM was the weakest one. The drawback of their

study is that they consider only binary classes. Even

if the dataset contained multi-class, they transformed

them into 2-class data sets. This could prevent the

learners from truly uncovering the impact of noise.

We note that some other papers analyze how class

noise can hamper the performance of some state-of-

the-art classiﬁcation models. Their results showed

that classiﬁcation performance is directly hampered

in the presence of class noise (Zhu and Wu, 2004),

(Nazari et al., 2018), (Pelletier et al., 2017), (Kala-

panidas et al., 2003). Nevertheless their experiments

did not include the impact of attribute noise. More-

over, they only focus on speciﬁc datasets such as im-

age datasets, and land cover mapping datasets (Nazari

et al., 2018), (da Costa et al., 2016).

3 METHODOLOGY

The quality of a dataset can be characterized by its

attributes and class labels (Nazari et al., 2018). This

section discusses two categories of noise and a brief

discussion about the classiﬁers we used in our exper-

iments.

3.1 Class Noise

Class noise is known as labeling errors when an in-

stance is incorrectly labeled. There are several causes

for class noise such as subjectivity during the labeling

process, data entry errors, or inadequacy of the infor-

mation used to label each example. There are two

types of class noise:

• Contradictory examples: Some examples appear

more than once with different class labels.

• Misclassiﬁcations: Some examples are labeled in-

correctly.

3.2 Attribute Noise

Attribute noise refers to corruption in the value of one

or more attributes. There are three types of attribute

noise:

• Erroneous attribute: The attribute with a wrong

value

• Missing attribute values: The value of an attribute

is unknown. Generally, it is represented as a “?”

sign.

• Don’t care values: The value of the attribute does

not affect the rest of the values in the example

3.3 Noise Injection

In the following we describe how we introduce noise

in class labels and attributes. It is difﬁcult to have real-

datasets where attribute and class noise are clearly

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164

Table 1: UCI Dataset Characteristics.

Dataset Instances Attributes Class Missing values Dataset characteristics Attribute characteristics Balanced?

Credit card 690 15 2 37 Multivariate Categorical, Integer, Real No

Iris 150 4 3 None Multivariate Real Yes

Spect 267 22 2 None Multivariate Categorical No

Glass 214 10 7 None Multivariate Real No

Wdbc 569 32 2 None Multivariate Real No

Wine 178 13 3 None Multivariate Integer,Real No

Dermatology 366 33 6 8 Multivariate Categorical, Integer No

Ecoli 336 8 8 None Multivariate Real No

Segmentation 2310 19 7 None Multivariate Real Yes

Yeast 1484 8 10 None Multivariate Real No

identiﬁed (Pelletier et al., 2017). To overcome such

limitations, we inject artiﬁcial noise into our datasets.

3.3.1 Attribute Noise Injection

We generate the attribute noise in the training data set

using Gaussian noise. Therefore, the values that the

noise can take on are Gaussian distributed. The prob-

ability density function of a Gaussian random variable

ζ is given by

p(ζ) =

√

2π

−(ζ−µ)

2σ

(1)

where µ is the mean of the distribution and σ is the

standard deviation.

3.3.2 Class Noise Injection

We followed the random noise model for class noise

injection. The percentage of noisy levels varies from

10% to 50%, with an interval of 10%. The class label

is changed from its current value to one of the other

possibly one, randomly. For instance, if there are 300

examples in a dataset, then adding a noise level of

10% implies that 30 labels will change randomly.

3.4 Choice of Classiﬁers

In the literature, there is a lack of work to analyze

the impact of noise on different types of classiﬁers.

Therefore, we select ﬁve classiﬁers for our experi-

ments. We divide our selected classiﬁers into two cat-

egories (i) Linear classiﬁers: NB and SVM, and (ii)

Non-linear classiﬁers: J48, RF, and k-NN.

The rationale for choosing the classiﬁers based on

their characteristics are three-fold: (i) Firstly, J48, k-

NN, and RF are non-linear classiﬁers that are useful

for problems that are linearly non-separable; i.e., the

class boundaries cannot be approximated well with

a planar surface. These boundaries can suffer from

overﬁtting. While there are methods such as prun-

ing in decision trees designed to reduce the chance

that the trees are overﬁtting (Quinlan, 2014), a nat-

ural question is how sensitive they are to noise in

data. Secondly, NB is a linear model for classiﬁca-

tion which leads to a linear decision boundary that

has been found effective in many problem domains.

Thirdly, each classiﬁer has its own inherent noise han-

dling mechanism. It allows us a better understanding

of the results of the impact of noise on them. For ex-

ample, the NB algorithm assumes each attribute is in-

dependent of each other. This could provide an added

advantage when noise is introduced to the dataset.

SVM when used to ﬁnd a linear boundary in the fea-

ture space (such as by using a polynomial kernel with

degree 1) can be considered to optimize the boundary

to be equally distant from the closest points of either

class. A comparison of NB with the linear SVM can

reveal whether the optimized boundary of the SVM

improves the handling of noise.

4 EXPERIMENTS AND RESULTS

A study on either class noise or attribute noise alone

cannot provide enough information about classiﬁer

behaviors against noise. Accordingly, a study of dif-

ferent types of noise on different learners is required

to achieve a meaningful conclusion while evaluating

classiﬁer behavior in noisy environments. Keeping

this in mind, we conduct our experiments (i) in pres-

ence of class noise and attribute noise (ii) use differ-

ent classiﬁers (iii) large training datasets (iv) different

characteristics of datasets. Our results provide an in-

sightful view of classiﬁcation performance in noisy

environments. In the following, we describe the main

aspects of the experimental results of this study.

4.1 Datasets

The experiments carried out in this paper are based

on 13 datasets of which 10 datasets are collected from

the UCI machine learning repository (Dua and Graff,

2017) and 3 publicly available image datasets. Table 1

summarizes the dataset characteristics from UCI and

Table 2 presents the details of the image datasets.

Noise in Datasets: What Are the Impacts on Classiﬁcation Performance?

165

The reason for choosing different datasets is to

investigate if the classiﬁcation performance is ham-

pered by the characteristics of the dataset. For in-

stance, missing values in a dataset are a common form

of attribute noise. Therefore, we deliberately choose

some datasets with missing values. It would be worth

experimenting to see how classiﬁers behave in such

a scenario. In addition, we focus on imbalanced data

sets. In such data sets, the distribution can vary from a

slight bias to a severe imbalance where there could be

one example in the minority class for hundreds, thou-

sands, or millions of examples in the majority of the

class or classes. For instance, the class distribution of

the wdbc data set is 62.74% for the positive class and

37.26% for the negative class. In imbalanced data,

the minority class is more sensitive than the majority

class. Therefore, we include both balanced and imbal-

anced datasets in our experiments. Fig. 1 illustrates

the class distribution for each UCI dataset included in

our experiments.

We also include large training datasets such as

CIFAR-10, MNIST, and Fashion-MNIST. As an ex-

ample, the training data for CIFAR-10 is 60,000.

Table 2: Characteristics of Image Datasets.

Dataset Training set Testing set class Balanced?

MNIST 60000 10000 10 No

Fashion-MNIST 60000 10000 10 Yes

CIFAR-10 50000 10000 10 Yes

4.2 Experimental Setup

In our experimental setup, we divide the UCI datasets

into three categories:

• Balanced dataset: Each class has equal distribu-

tion (iris, and segmentation dataset)

• Slightly balanced dataset: The distribution of

classes is uneven by a small amount. In our set-

ting, if the majority class to minority class ratio is

between 1:1 to 1:69, we deﬁne it as a slightly im-

balanced dataset (credit card, spect, glass, wdbc,

wine, and dermatology dataset)

• Highly imbalanced dataset: The distribution of

classes is uneven by a large amount. In our set-

ting, if the majority class to minority class ratio is

greater than 1:70, we deﬁne it as a highly imbal-

anced dataset (ecoli, and yeast dataset)

To evaluate the performance of noise on classiﬁca-

tion performance, we use 2 different evaluation met-

rics :

• AUROC: The AUROC computes the area under

the ROC curve. The ROC curve plots the true pos-

itive rate vs false positive rate at various threshold

settings. In our experiments, we use AUROC for

balanced and slightly imbalanced datasets. This is

because the AUROC gives the same result regard-

less of what the class probabilities are.

• AUPRC: AUPRC is deﬁned as the average of pre-

cision scores calculated for each recall threshold.

We use AUPRC for highly imbalanced datasets as

it focuses mainly on the positive class.

In the case of image datasets, we use the loss func-

tion to evaluate the performance of the deep neural

network. The loss function we use in our experiments

is categorical cross-entropy.

We split our dataset into training and test sets. To

preserve the percentage of samples for each class, we

use a variation of K-fold named stratiﬁed K-fold. The

value for K is set to 10 because the low values of K

will result in a noisy estimate of model performance

and a very large value will result in a less noisy esti-

mate of model performance.

When simulating class noise, the training dataset

is corrupted with varying degrees of noise while keep-

ing the test dataset clean. It allows us to evaluate the

true performance of the classiﬁer. In the case of class

noise, random noises are injected with rates of 10%,

20%, 30%, 40%, and 50%. We restrict our noise level

up to 50% of the original dataset because in realistic

situations only certain types of classes are likely to

be mislabeled. For attribute noise, we use Gaussian

noise with zero mean and 2 different variance values

of 0.5 and 0.7. In image datasets, we vary the value

of variance from 0.1 to 0.9. The reason is that with a

small variance a noisy image can still have good per-

formance and the distortion level will be minimum.

Hence, we want to observe the performance with dif-

ferent variances of noise. For image datasets, we cor-

rupted the training data with Gaussian noise and eval-

uated it with test data.

For class noise evaluation, we use the Weka tool

(Eibe et al., 2016). It is a free software tool for data

mining tasks. In the case of attribute noise evalua-

tion, we implemented the noise injection model and

the classiﬁers in Python 3.5. The parameter settings

for ﬁve classiﬁers are as follows: J48 (conﬁdence

factor C=0.25), NB (bacthSzie=100, useKernelEsti-

mator=False), SVM (kernel: polynomial kernel with

degree 1, tolerance parameter=0.001), k-NN (k=1,

distance: euclidean distance) and RF (bagSizePer-

cent=100, maxDepth=0, numIterations=100). All the

experiments were run on Mac OS Big Sur with a 3.1

GHz CPU and 8GB memory.

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166

Figure 1: Class Distribution of UCI Datasets.

Table 3: Rank of Classiﬁers.

Classiﬁers 10% Noise 20% Noise 30% Noise 40% Noise 50% Noise Avg Score Rank

J48 0.74 0.66 0.56 0.50 0.46 0.58 4

NB 0.79 0.71 0.64 0.57 0.50 0.64 2

SVM 0.76 0.68 0.61 0.54 0.49 0.61 3

K-NN 0.70 0.61 0.54 0.49 0.45 0.56 5

RF 0.81 0.73 0.64 0.57 0.51 0.65 1

4.3 Results

We evaluated our experimental results in presence of

attribute noise and class noise. For each type of noise,

we use the same ﬁve classiﬁers. The following sub-

section presents the results of various experiments.

4.3.1 Effects of Class Noise

In our ﬁrst analysis, we compare the performance of

different classiﬁers in the presence of varying degrees

of class noise. We use the same classiﬁers for each

of the settings. The datasets used in this setting are

from the UCI repository. They have been testiﬁed

to be appropriate in many algorithms in the litera-

ture(Libralon et al., 2009). The typical assumption is

that these datasets are clean and noise-free(Libralon

et al., 2009). But there are missing values in these

datasets. In our experiments, we replace all miss-

ing values for nominal and numeric attributes in a

dataset with the modes and means from the training

data. Firstly, we train and test each dataset using our

selected ﬁve classiﬁers. Then we gradually increase

the noise level to test how these classiﬁers behave.

Noise in Datasets: What Are the Impacts on Classiﬁcation Performance?

167

(a) J48 (b) NB (c) SVM

(d) K-NN (e) RF

Figure 2: Effects of class noise on different classiﬁers. The y axis represents the AUROC score except for Ecoli and Yeast.

AUPRC score is reported for Ecoli and Yeast dataset. Six color bar represents the prediction accuracy when six different noise

labels are applied for each dataset.

Fig. 2 shows the classiﬁcation results obtained by

J48, NB, SVM, k-NN, and RF. As can be seen from

the ﬁgures, the classiﬁcation performance drops sig-

niﬁcantly with an increase in noise. We observed that

this statement is true for all classiﬁers in spite of the

classiﬁers’ inherent noise handling mechanisms. One

key observation is that: when analyzing the situa-

tion with high levels of noise, the classiﬁers fail to

learn from the data and make the classiﬁcation task

difﬁcult. For example, when the degree of noise is

50%, we see a signiﬁcant drop in the prediction per-

formance. The score is below 60% for almost all of

the datasets. However, this is expected behavior since

it is harder to classify the data due to high levels of

noise. However, from Fig. 2, it is also clear that even

in the presence of a little noise, the performance of

each classiﬁer is hampered.

We also computed the average rank of each clas-

siﬁer. Firstly, we computed the average score of each

classiﬁer. For instance, the average score of J48 clas-

siﬁer in different noise levels are 0.74(10% noise),

0.66(20% noise), 0.56(30% noise), 0.50(40% noise)

and 0.46(50% noise). We rank the learners accord-

ing to the prediction score. The highest value of

prediction score ranks ﬁrst. With a reference to Ta-

ble 3, RF ranks ﬁrst while the last rank belongs to

k-NN. It is important to notice that in the case of low

noise(10%), the rank remains the same but with a high

level of noise(50%) NB performs very well compared

to the other classiﬁers except RF. However, k-NN al-

ways demonstrates the worst performance in every

setting. Another important observation is that the per-

formance of linear classiﬁers (average score is 0.63)

is better than non-linear classiﬁers ( average score is

0.60)

4.3.2 Effects of Attribute Noise

To evaluate the impact of attribute noise, we use

the same datasets used for class noise evaluation.

The prediction score obtained without noise and with

noise are illustrated in Fig. 3. In presence of attribute

noise, on 7 out of 10 datasets, the prediction perfor-

mance degrades for each classiﬁer. The exception

is for wine, dermatology, and segmentation dataset

when we use NB and RF classiﬁer model. It is impor-

tant to note that the performance degrades very little

when we increase the variance from 0.5 to 0.7. We

also presented a ranking of classiﬁers in Table 4. We

followed the similar ranking procedure described in

the previous paragraph. The highest prediction score

comes from the classiﬁer RF and the lowest from k-

NN. We also observe that the performance of linear

classiﬁers (average score is 0.74) is better than non-

linear classiﬁers (average score is 0.70).

ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods

168

(a) J48 (b) NB (c) SVM

(d) K-NN (e) RF

Figure 3: Effects of attribute noise on different classiﬁers. The y axis represents the AUROC score except for Ecoli and Yeast.

AUPRC score is reported for Ecoli and Yeast dataset.

Table 4: Rank of Classiﬁers.

Classiﬁers Noise(σ

=0.5) Noise(σ

=0.7) Avg Rank

J48 0.69 0.68 0.69 4

NB 0.76 0.75 0.76 2

SVM 0.73 0.72 0.73 3

K-NN 0.66 0.66 0.66 5

RF 0.77 0.76 0.77 1

4.3.3 Effects of Noise in Deep Learning

We conduct another set of experiments to evaluate the

impacts of noise on deep neural networks for multi-

classiﬁcation tasks. Deep neural networks are capa-

ble of generalizing from training data(Nazar

e et al.,

2017). So, our goal of this experiment is: can

deep neural networks still be able to generalize af-

ter training on noisy data? Fig. 4 demonstrates the

results obtained from three image datasets: MNIST,

Fashion-MNIST, and CIFAR-10. We inject Gaussian

noise in each of the datasets with a variance ranging

from 0.1 to 0.9. We use Convolution Neural Net-

work (CNN) with the following parameter settings:

(i) model: sequential (ii) activation function: relu, and

softmax (iii) loss function: categorical cross-entropy

and (iv)optimizer:adam. From Fig. 4, we can see that

the performance drops in case of low variance as well

as high variance. Therefore, we can conclude that

deep neural networks fail to generalize in presence of

noise.

Figure 4: Effect of noise on MNIST, Fashion-MNIST and

CIFAR-10 dataset.

4.3.4 Key Takeaways from Our Results

We observe some interesting facts from our results.

The key observations from our experiments are

• Class noise degrades the classiﬁcation perfor-

mance

• The attribute noise is also harmful and could bring

severe problems to classiﬁers

• Class noise is more dangerous than attribute noise

• Random forest is more resilient to noise and k-NN

is the weakest one

• Linear classiﬁers are more tolerant to noise than

non-linear classiﬁers

• Deep neural network ﬁnds difﬁculty to generalize

after training on massively noisy data

Noise in Datasets: What Are the Impacts on Classiﬁcation Performance?

169

5 CONCLUSIONS

This paper investigated the impacts of noise on var-

ious classiﬁers in various environment settings. We

analyzed how attribute noise and class noise affect the

quality of the models. The ﬁve well-known classiﬁers

including J48, NB, SVM, k-NN, SVM, and RF have

been compared with different noise levels. The gen-

eral results show that both types of noise have adverse

effects on each classiﬁer. The simple observation is

that RF is the best learner and k-NN gives the worst

performance in a noisy environment. Another obser-

vation is that deep neural networks are not robust to

noise. Our experimental results may serve either as a

guideline for the selection of appropriate classiﬁers in

noisy environments or for developing noise handling

mechanisms.

ACKNOWLEDGMENTS

This work is supported by the U.S. National Science

Foundation under grant number OIA-1946231 and

the Louisiana Board of Regents for the Louisiana Ma-

terials Design Alliance (LAMDA).

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