# Gradient Boosting-Based Predictive Click Fraud Detection Using Manifold Criterion Variable Elimination

Lokesh Singh, Deepti Sisodia, N. L. Taranath

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Lokesh Singh, Deepti Sisodia, N. L. Taranath. Gradient Boosting-Based Predictive Click Fraud Detection Using Manifold Criterion Variable Elimination. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.287-299, 10.1007/978-3-031-38296-3_22. hal-05466049

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# Gradient Boosting-Based Predictive Click Fraud Detection Using Manifold Criterion Variable Elimination

Lokesh Singh(✉), Deepti Sisodia, and N. L. Taranath

Department of Computer Science and Engineering, Alliance College of Engineering and Design, Alliance University, Bangalore, Karnataka, India
{lokesh.singh,deepti.sisodia,taranathnl}@alliance.edu.in

**Abstract.** Online advertising models are vulnerable to click fraud, which occurs when an individual or a group repeatedly clicks on an online advertisement with the intent to generate illegitimate clicks and make money from the advertiser. In machine learning-based approaches for detecting click fraud, the performance of the models can be affected by the presence of collinear, redundant, and least significant features in the dataset. These types of features can lead to overfitting, where the model becomes too complex and fails to generalize well to new data. Therefore, a Manifold Criterion Variable Elimination method is proposed in this work to select significant features utilizing the potential of six filter-based feature selection techniques for the discrimination of fraud and genuine publishers. Experimentations are conducted on the online advertisement user click dataset in two modes, first considering all extracted features and second considering only selected features. An extraction of 103 statistical features from the user-click dataset is performed for each class instance labelled with OK, Fraud and Observation. The Manifold Criterion Variable Elimination method selects the top 15 most relevant features. Individual and ensemble learning models are trained with selected feature-set and tuned parameter values. The performances of learners are evaluated using standard evaluation measures. The results demonstrated that, in general, the performance of all learners improved with the selected feature set. Particularly, the Gradient Tree Boosting (GTB) ensemble model performed superiorly by improving the weak learners by minimizing the model's loss via merging the weak learners into a strong one iteratively.

**Keywords:** PPC model · Illegitimate clicks · Legitimate clicks · Ensemble Methods · Feature Selection · GTB

## 1 Introduction

Online advertising campaigns have become increasingly important in the digital advertising industry due to the fast development of the internet. The Pay-Per-Click (PPC) pricing model is one of the most popular pricing models for online advertising campaigns [1]. In the PPC model, advertisers pay a commission based on the number of
clicks generated by their ads. These clicks can be generated either by potential cus-
tomers or by automated computer programs. While legitimate clicks are generated by
users having interest in the advertised product or service, illegitimate clicks are generated
by individuals or groups with the intention of fraudulently increasing the click count to
increase the advertiser's cost [2, 3]. The Advertising Commissioner acts as a middleman
in the process, connecting advertisers and publishers to help facilitate the process of
buying and selling advertising space. The advertiser provides the advertisements and
provides the revenue per click generation, while the publisher provides the space on
their website for the ads to be displayed. The commission received by the advertising
commissioner helps to cover their costs and compensation for the services they provide.
The overall goal of the advertising commissioner is to ensure that both the advertiser
and the publisher are satisfied with the results of the advertising campaign and that the
process runs smoothly and efficiently [4, 5]. Click fraud is a significant challenge in the
internet marketing field because it can lead to financial losses for advertisers who are
paying for clicks that do not result in any legitimate business [6]. One of the challenging
issues addressed in this field is that if all the clicks are deemed false, then it becomes
unjustifiable for those advertisers who visit the same advertisement link multiple times
a week. The goal of this experiment is to describe the challenges of this domain for big
datasets. Several machine learning models [7] are compared and evaluated to analyse the
publisher's behaviour as OK, Observation or Fraud. Click fraud has now become a severe
threat to the online advertising business. Researchers have used various techniques to
extract features related to publishers' behavior and analyze their click patterns to identify
potential click fraud. For example, Berar et al. [6], Perera et al. [8], and Phua et al. [9]
have used machine learning-based approaches to extract features such as the number of
clicks per user, the average time spent on a page, the number of unique users, and the ratio
of clicks to impressions, among others, to detect fraudulent activity. In the context of
mobile advertising, Vasumati et al. [10] have developed a method to detect click-spam, a
type of click fraud where publishers generate clicks on ads by using automated software
on mobile devices. They use features such as the frequency of clicks, the duration of
clicks, and the time between clicks to identify click-spam and block it in real-time. Fea-
ture selection and sampling strategies are left uncovered in this experiment which might
improve the model's performance. Berar et al. [11] proposed a novel method for gener-
ating click patterns for each publisher using machine learning-based approaches. They
extracted various features related to publishers' behavior, such as the number of clicks
per user, the time of day when clicks occur, and the frequency of clicks from a single IP
address, among others. They then used these features to generate click patterns for each
publisher and train a machine learning model to detect fraudulent activity. However,
the authors did not use variable elimination techniques in their approach, which could
potentially enhance the performance of their model by removing collinear or irrelevant
features that may reduce the model's accuracy. Taneja et al. [12] addressed this issue and
proposed a method that uses principal component analysis (PCA) to eliminate collinear
features and select the most important features for training the machine learning model.
Sisodia et al. play a vital role in the detection of click fraud. They extracted 103 fea-
tures from the existing attributes and utilized the potential of GTB [13] to discriminate
fraudsters from honest publishers. Further, they resolved the issue of a fair balancing of
class distributions by assessing the influence of data-level sampling techniques [14] on the performance learning models towards obtaining a fair balancing of class samples in the dataset. Extending their work, they further balanced the imbalance class distribution by designing a k-NN-based under-sampling strategy [15]. Besides employing data-level sampling methods, they enhanced the classification performance by designing a novel hybrid sampling strategy [16] that balances the dataset by concatenating the potentials of over and under-sampling. Moreover, for the better discrimination of fraudsters they investigated optimal features from the existing attributes [17], designed new conditional features [18] that helped in capturing the conduct of fraudulent publishers. Furthermore, for automated feature extraction they designed a deep CNN [19] based methodology that has transformed user click features into an image. The use of automated feature extractors to identify publisher behavior can have a significant impact in several ways. This research work aims to assess the efficacy of the GTB model in detecting click fraud in online advertising by identifying fraudulent behavior of publishers. The study highlights the importance of using appropriate machine learning models and variable elimination methods to detect click fraud in web advertising. Class imbalance is one of the biggest challenges in the user click dataset used in detection of click fraud. However, the proposed work in this research does not use data-level sampling. This is because the GTB model used in the proposed work could handle imbalanced data and can acquire superior performance even with imbalanced datasets. This is because the GTB model is an ensemble model that uses multiple decision trees, and each tree can focus on different regions of the feature space and different patterns in the data. Some of the key contributions of this research work are:

* A Manifold-criterion variable elimination method is proposed to extract significant features from the existing features towards analyzing the fraudulent behaviour of publishers.

* The extraction of 103 features per publisher using composite attributes, helped to identify suspicious behavior more effectively. The use of composite attributes, which merge two or more attributes, has provided a more comprehensive understanding of the publisher's behavior, allowing for more accurate detection of any suspicious activities.

* Commonly ranked and highly voted 15 informative features were extracted utilizing the potentials of six filter-based feature selection methods.

* Several individual and ensemble-based learning models are utilized to evaluate the impact of the designed methodology, wherein Gradient Tree Boosting has achieved the highest average precision score with the topmost significant 15 features.

The remaining work is structured as follows: methods and material are discussed in Sect. 2, which elaborates further sections: dataset details, preprocessing strategies, learning models for classification, validation and evaluation metrics; Sect. 3 elaborates the experimental results and discussions in detail while final work is concluded in Sect. 4.
# 2 Methods and Material

## 2.1 Block Diagram

A graphical representation of the proposed methodology is outlined in Fig. 1 as a block diagram. The system’s functional view provides a clear illustration of the flow of experiments conducted in model design.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Block diagram of the proposed methodology</figcaption></figure>

## 2.2 Data Acquisition

Mobile Advertising user-click data (FDMA2012) [9] is used for this experiment. The data used to analyze click fraud includes both publisher and click datasets in CSV format. The publisher’s dataset comprises details about the publishers, such as their website URL, the number of unique visitors, and the amount of clicks generated by each publisher, among others. The click dataset contains details about the clicks associated with each publisher, such as the timestamp of the click, the IP address of the user, and the user agent used to access the publisher’s website, among others. Table 1 represents the dataset’s statistics.
<table><caption>**Table 1.** Statistics of the dataset</caption><thead><tr><th rowspan="2">Dataset</th><th rowspan="2">Time Period</th><th rowspan="2">No of clicks</th><th colspan="4">No. of Publishers</th></tr><tr><th>Fraud</th><th>Observation</th><th>OK</th><th>Total</th></tr></thead><tbody><tr><td>Train set</td><td>9–11 FEB 2012</td><td>1048575</td><td>72 (2.34%)</td><td>80 (2.60%)</td><td>2,929 (95.07%)</td><td>3,081</td></tr><tr><td>Validation set</td><td>23–25 FEB 2012</td><td>1048575</td><td>85 (2.77%)</td><td>84 (2.74%)</td><td>2,895 (94.48%)</td><td>3,064</td></tr><tr><td>Test set</td><td>8–10 MAR 2012</td><td>1048575</td><td>82 (2.73%)</td><td>71 (2.37%)</td><td>2,847 (94.90%)</td><td>3,000</td></tr></tbody></table>

## 2.3 Preprocessing

Since the provided raw data used in this experiment is inconsistent with missing values, feature extraction is performed to preprocess the data, wherein a total of 103 distinct features are found to be predictive for the experiment. To improve model performance, reduce overfitting, and simplify model interpretation, variable elimination is thus used for electing the most relevant features from a user click dataset and eliminating the irrelevant or redundant ones.

## 2.4 Proposed Manifold Criterion Variable Elimination Method

The concept behind variable elimination is to select a subset of existing features by alienating those features which are less/not predictive, insignificant and redundant. Feature selection techniques based on the filter approach are efficient comparatively wrapper methods. The central issue addressed with wrapper methods [20] is that though they achieve higher accuracy but suffer from high computational complexity [21] and large computation time [22]. While the filter method requires less computation time, averts overfitting and provides better generalization to datasets [23]. Thus, we designed a manifold criterion variable elimination method which works in two phases.

**Ist Phase:** Six filter-based feature selection methods are employed namely Correlation-based subset feature selection (Cfs), Symmetric uncertainty (SU), Gain ratio (GR), ReliefF (RF), Information gain (IG), Correlation-based feature selection (CFS) to obtain the topmost significant selected features, which are shown in Table 2.

**IInd Phase:** Table 2 depicts significant features chosen by various feature selection methods which are marginally distinct. For example, feature F21 is ranked as the most significant feature measured by the 'Symmetrical Uncertainty' technique while the 'Gain Ratio' does not select it' and 'CfsSubset' feature selection methods. It can be observed from Table 2 that optimal features selected using one evaluation method are not similar to that of another. Therefore, choosing optimal features using only one evaluation method may miss the investigation of many significant features that might affect classification accuracy. F71 and F17 are the topmost common relevant features elected by all the variable elimination methods. Therefore, among all the significant features elected by
<table><caption>**Table 2.** Significant features obtained by various feature selection methods.</caption><thead><tr><th>Type of FS</th><th>FS Method</th><th>Topmost Selected Features</th></tr></thead><tbody><tr><td rowspan="6">Filter</td><td>GainRatio</td><td>F4, F12, F6, F7, F17, F92, F71, F25, F85, F100, F96, F53, F93, F26, F103, F101, F8, F2, F79, F91, F86, F99</td></tr><tr><td>InfoGain</td><td>F12, F102, F86, F1, F26, F92, F96, F53, F27, F71, F101, F17, F100, F9, F99, F25, F103, F2, F93, F4, F91, F21, F10, F8, F85</td></tr><tr><td>CFS</td><td>F23, F47, F99, F102, F10, F43, F71, F8, F17, F12, F92, F11, F91, F103, F19, F53, F85, F6, F79, F101, F9, F50, F93, F2, F14, F4, F86, F96, F100</td></tr><tr><td>ReliefF</td><td>F102, F12, F87, F103, F99, F17, F86, F84, F91, F82, F100, F92, F85, F90, F96, F2, F71, F4, F53, F79, F9, F81, F8, F75, F83, F93, F101, F54, F28, F65</td></tr><tr><td>Symmetrical Uncertainty</td><td>F21, F102, F100, F10, F85, F3, F25, F20, F19, F91, F24, F16, F14, F13, F92, F53, F2, F71, F103, F23, F9, F22, F11, F79, F15, F18, F17, F7, F4, F5, F93, F86, F45, F13, F99, F26, F101, F96, F12, F6, F43, F8</td></tr><tr><td>CfsSubset</td><td>F1, F71, F10, F17</td></tr></tbody></table>

FS – Feature Selection.

six variable elimination methods, features commonly selected by at least half of the FS methods are considered significant features. A total of 15 commonly voted highly ranked features are selected as final features using the manifold-criterion feature selection process for further processing, which is depicted in Table 3. These topmost 15 features are then fed as input to different classifiers.

<table><caption>**Table 3.** Best 15 significant features obtained by the manifold-criterion feature selection method.</caption><thead><tr><th>Method</th><th>Best Selected Features</th></tr></thead><tbody><tr><td>Manifold-Criterion Variable Elimination</td><td>F17, F71, F53, F96, F93, F91, F99, F100, F86, F92, F12, F2, F101, F85, F103</td></tr></tbody></table>

Details of the top 15 significant features and their voting score are discussed in Table 4. Voting score 6 indicates that all six methods select the feature while the features selected by five and four FS methods are voted as 5 and 4, respectively. Figure 2 depicts the best 15 relevant features obtained by the manifold-criterion feature selection method.

## 2.5 Learning Models

To investigate click fraud, other than GTB, following learning models are employed in experiment: K-Nearest Neighbor [24, 25], Decision Tree [26, 27], Discriminant Analysis [28, 29], Naïve Bayes [30, 31], Support Vector Machine [32–35], AdaBoost-M2 [36,
<figure><img src="image_5.png" /><figcaption>Fig. 2. Best 15 significant features selected using manifold criterion.</figcaption></figure>

<table><caption>Table 4. Details of the final selected 15 features</caption><thead><tr><th>S.No</th><th>Selected Attributes</th><th>Voting Score</th><th>Name of Attribute</th><th>S.No</th><th>Selected Attributes</th><th>Voting Score</th><th>Name of Attribute</th></tr></thead><tbody><tr><td>1</td><td>F17</td><td>6</td><td>std_spiky_iplong</td><td>9</td><td>F86</td><td>5</td><td>brand_Apple_percent</td></tr><tr><td>2</td><td>F71</td><td>6</td><td>std_total Clicks</td><td>10</td><td>F92</td><td>5</td><td>avg_per_hour_density</td></tr><tr><td>3</td><td>F53</td><td>5</td><td>distinct_iplong</td><td>11</td><td>F12</td><td>5</td><td>avg_spiky_ReAgCnIp</td></tr><tr><td>4</td><td>F96</td><td>5</td><td>cntr_in_percent</td><td>12</td><td>F2</td><td>5</td><td>avg_spiky_ReAgCnIpCi</td></tr><tr><td>5</td><td>F93</td><td>5</td><td>std_per_hour_density</td><td>13</td><td>F101</td><td>5</td><td>cntr_th_percent</td></tr><tr><td>6</td><td>F91</td><td>5</td><td>brand_other_percent</td><td>14</td><td>F85</td><td>5</td><td>brand_Generic_percent</td></tr><tr><td>7</td><td>F99</td><td>5</td><td>cntr_tr_percent</td><td>15</td><td>F103</td><td>4</td><td>cntr_uk_percent</td></tr><tr><td>8</td><td>F100</td><td>5</td><td>cntr_ru_percent</td><td></td><td></td><td></td><td></td></tr></tbody></table>

[37], Random Forest [9, 38], LpBoost [39, 40], TotalBoost [41, 42]. Out of numerous classifiers, GTB has surpassed the results of other classification methods in the experiment. The success of GTB in this experiment is attributed to its constructive approach in ensemble generation. By combining the predictions of multiple weak models, the GTB method has overcome the limitations of individual models and produced more accurate predictions. GTB's constructive method in ensemble generation has made the results superior to other classification methods.

## Gradient Tree Boosting

The goal of GTB is to find an estimation function $F(x)$ that can accurately predict the output variable $y$ given an input variable $x$. The GBM approach achieves this by iteratively adding weak learners $h_i(x)$ from a predefined set of weak learners $\mathcal{H}$, to improve the predictions of the previous iterations.

The method starts by initializing the model with a constant function $F_0(x)$ that is the best fit to the training data in terms of the loss function. In each iteration, the model updates the parameters by computing the loss function's gradient corresponding to the parameters and updating the parameters in the direction that reduces the loss function. The parameters are updated by finding the optimal step size $\gamma$ that minimizes the loss function.

The final estimation function $\hat{F}(x)$ is a weighted sum of the weak learners $h_i(x)$, where the weights are determined by the optimal step sizes found in each iteration. This
method has been shown to be effective in reducing the empirical risk and improving the accuracy of the predictions.

## 2.6 Cross-Validation and Evaluation Measures

10-Fold cross-validation [43] is used to evaluate the performance of a model on the FDMA 2012 dataset. Traditional learning models do not generalize well regarding accuracy in case of imbalanced datasets. Thus, the evaluation of learning models is performed using Average Precision, Recall and F1-score instead of accuracy [44]. Table 5 illustrates evaluation measures for multiclass classification.

<table><caption>**Table 5.** Evaluation measures</caption><thead><tr><th>Evaluation Measures</th><th>Formula</th></tr></thead><tbody><tr><td>Average Precision (AP)</td><td><sup>Σ<sub>i=1</sub><sup>l</sup> <i>t</i><sub><i>p</i><sub>i</sub></sub></sup>/<sub><i>l</i></sub></td></tr><tr><td>Recall</td><td><sup><i>t</i><sub><i>p</i><sub>i</sub></sub></sup>/<sub><i>t</i><sub><i>p</i><sub>i</sub></sub> + <i>f</i><sub><i>n</i><sub>i</sub></sub></sub></td></tr><tr><td>F1-Score</td><td>2 * <sup>precision*recall</sup>/<sub>precision + recall</sub></td></tr></tbody></table>

## 3 Experimental Results and Discussion

This section summarizes the results obtained during the experiment. In this work, we have analyzed fraudulent behavior of publishers in PPC (Pay-Per-Click) advertising, by dividing the day into four six-hour periods: morning, afternoon, evening, and night. Within each six-hour period, we have further segment time into four intervals of fifteen minutes each based on the number of minutes elapsed: the 1st interval includes the first 0–14 min, the 2nd interval includes the 15–29 min, the 3rd interval includes the 30–44 min, and the 4th interval includes the 45–59 min. This segmentation approach proves to be useful for identifying patterns or trends in fraudulent activity over time. The effectiveness of designed features is assessed utilizing several individual and ensemble classification algorithms, which is demonstrated in Table 6 and is evaluated using several adopted measures like Average-Precision (AP), Recall (RE) and F1-score (F1) using all and selected features. For KNN (K-Nearest Neighbour), the parameter k is chosen to 2, which gives a good average precision score on the validation dataset with all features. The ‘Distance’ and ‘Discrim Type’ functions of KNN and QDA are also tuned for their predictions. SVM has also experimented with kernels like Linear.

The performance of various classifiers was evaluated on a dataset with a large number of features, and the Decision Tree classifier performed the best among individual classifiers. Ensemble methods were able to achieve higher accuracy than individual classifiers due to the combination of multiple classifiers. However, none of the model were able to
<table><caption>**Table 6.** Performance of individual and ensemble classifiers on validation dataset with all and selected features</caption><thead><tr><th rowspan="3">Methods</th><th colspan="6">Validation Dataset</th></tr><tr><th colspan="3">All Features</th><th colspan="3">Selected Features</th></tr><tr><th>AP</th><th>RE</th><th>F1</th><th>AP</th><th>RE</th><th>F1</th></tr></thead><tbody><tr><td><strong>Gradient Tree Boosting</strong></td><td><strong>60.5</strong></td><td><strong>57.8</strong></td><td><strong>59.1</strong></td><td><strong>64.86</strong></td><td><strong>58.3</strong></td><td><strong>60.2</strong></td></tr><tr><td>Random Forest</td><td>56.2</td><td>52.2</td><td>54.1</td><td>61.13</td><td>55.1</td><td>57.3</td></tr><tr><td>Logistic Regression</td><td>49.6</td><td>45.2</td><td>47.3</td><td>53.2</td><td>48.7</td><td>50.2</td></tr><tr><td>K-Nearest Neighbor</td><td>49.6</td><td>44.3</td><td>46.8</td><td>54.75</td><td>50.8</td><td>53.3</td></tr><tr><td>Decision Tree</td><td>53.2</td><td>50.1</td><td>51.6</td><td>54.81</td><td>48.4</td><td>50.6</td></tr><tr><td>Naïve Bayes</td><td>35.2</td><td>30.7</td><td>32.8</td><td>40.57</td><td>35.7</td><td>32.6</td></tr><tr><td>Support Vector Machine (Linear)</td><td>38.1</td><td>36.4</td><td>37.2</td><td>39.25</td><td>35.8</td><td>37.2</td></tr><tr><td>Linear Discriminant Analysis</td><td>38.1</td><td>36.4</td><td>37.2</td><td>46.6</td><td>41.5</td><td>43.9</td></tr><tr><td>Quadratic Discriminant Analysis</td><td>48.6</td><td>45.3</td><td>47.2</td><td>52.62</td><td>47.8</td><td>50.7</td></tr><tr><td>AdaBoost</td><td>50.6</td><td>43.5</td><td>45.8</td><td>55.9</td><td>49.9</td><td>52.8</td></tr><tr><td>LPBoost</td><td>56.7</td><td>54.9</td><td>55.6</td><td>58.75</td><td>53.7</td><td>54.7</td></tr><tr><td>Total Boost</td><td>52.6</td><td>47.6</td><td>49.7</td><td>55.54</td><td>50.6</td><td>51.9</td></tr></tbody></table>

achieve precision higher than 60.5% on the validation dataset using all features. Thus, feature selection was performed to improve the model's performance, as not all features were useful. After feature selection, Gradient Tree Boosting (GTB) outperformed other classifiers by achieving an average precision of 65.6% on the validation dataset. The results suggest that the removal of unwanted, duplicate, and inconsequential features has improved the classification performance. Additionally, Table 7 compares the proposed approach with previous methods and shows that the proposed Gradient Tree Boosting approach achieved the highest average precision score.
<table><caption>**Table 7.** Analyzing previous and proposed work.</caption><thead><tr><td>Author</td><td>Classification Models</td><td>Feature Selection</td><td>Result (AP)</td></tr></thead><tbody><tr><td>Perara et al. [8]</td><td>Bagging + Random Forest</td><td>YES</td><td>51.40%</td></tr><tr><td>Vasumati et al. [10]</td><td>Random Forest</td><td>NO</td><td>52.30%</td></tr><tr><td>Taneja et al. [12]</td><td>RFE + HDDT</td><td>YES</td><td>64.07%</td></tr><tr><td>Berrar [6]</td><td>Random Forest</td><td>NO</td><td>49.99%</td></tr><tr><td>Berrar [11]</td><td>Random Forest</td><td>NO</td><td>36.20%</td></tr><tr><td>Oentaryo et al. [9]</td><td>Random Forest</td><td>YES</td><td>58.84%</td></tr><tr><td>Sisodia et al. [13]</td><td>Gradient Tree Boosting</td><td>NO</td><td>60.51%</td></tr><tr><td><strong>Proposed Work</strong></td><td><strong>Gradient Tree Boosting</strong></td><td><strong>YES</strong></td><td><strong>65.60%</strong></td></tr></tbody></table>

## 4 Conclusion

Pay-Per-Click fraud is a serious issue in online advertising, and detecting illegitimate publishers is a critical step in preventing fraudulent activity. This research work aims to assess the efficacy of the GTB model to detect click fraud in online advertising by identifying suspicious conduct of publishers. The efficacy of the designed predictive model was validated by evaluating several machine learning classification approaches together with distinct ensemble methods using all features and selected features on the validation dataset. The research has yielded positive results in terms of Average-Precision, Recall, and F1-score. The use of GTB has been shown to be effective in many different contexts, including fraud detection. By combining multiple models in a boosting approach, GTB is able to reduce prediction errors and improve the overall accuracy of the model. It has achieved an average precision score of 65.6%, the best AP score with selected features on the dataset. Variable elimination as a data-preprocessing step thus proves to be an apparent and efficacious strategy in model designing, improving the classification performance. Classification problems where classes have unequal representation are demonstrated as imbalance classifications where the number of samples belonging to one class is significantly less than the samples belonging to another class which in turn causes the classifier to be biased towards the majority class. So, despite significant results and investigations performed to identify fraudsters, there exists a substantial need for further work on click fraud. Since the dataset is highly imbalanced, future analysis is thus pivotal to validate the methods like up-sampling, down-sampling, etc., for better results which are left uncovered in this experiment.

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