Comparison of data mining classification algorithms for breast cancer prediction

Shah, Chintan; Jivani, Anjali

doi:10.1109/icccnt.2013.6726477

Cited by 73 publications

(28 citation statements)

References 3 publications

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“…Three different data mining classification algorithms for prediction namely decision tree, Naïve Bayes, and K-Nearest Neighbor [8] with the help of WEKA (Waikato Environment for Knowledge Analysis),which is an open source software, have been compared for prediction of cancer. It has been concluded that Naïve Bayes is a superior algorithm compared to the two others.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach towards Crimes against Women using Time Series Algorithm

Motwani¹,

Purwar²,

Mathur³

et al. 2018

IJCA

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One of the major issues in every nation these days is the rise in crime against women. Every day we come across various cases of abuse against women. Study of past crime data can help us in analysing crime patterns and important hidden relations between the crimes. So, crimes predicting model can be simulated which will study verified past crime records and predict future criminal activities. In recent past, there has been an increased interest in time series research. This has been used particularly for finding useful similar trends in multivariate time series in various applied fields such as environmental research, agriculture, sales and finance. This paper elaborates upon the use of time series algorithm in accurately predicting and extracting patterns that occur frequently within a dataset to obtain useful hidden information.

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Section: Related Workmentioning

confidence: 99%

An Efficient Approach towards Crimes against Women using Time Series Algorithm

Motwani¹,

Purwar²,

Mathur³

et al. 2018

IJCA

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“…The unknown tuple in K-NN is assigned to most common class among its K-nearest neighbors. When K = 1, the unknown tuple is assigned the class of the training tuple that is closest in the pattern space [28].…”

Section: K-nearest Neighbormentioning

confidence: 99%

A comparative study on machine learning algorithms for indoor positioning

Bozkurt¹,

Elibol

Günal

et al. 2015

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)

117

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Fingerprinting based positioning is commonly used for indoor positioning. In this method, initially a radio map is created using Received Signal Strength (RSS) values that are measured from predefined reference points. During the positioning, the best match between the observed RSS values and existing RSS values in the radio map is established as the predicted position. In the positioning literature, machine learning algorithms have widespread usage in estimating positions. One of the main problems in indoor positioning systems is to find out appropriate machine learning algorithm. In this paper, selected machine learning algorithms are compared in terms of positioning accuracy and computation time. In the experiments, UJIIndoorLoc indoor positioning database is used. Experimental results reveal that k-Nearest Neighbor (k-NN) algorithm is the most suitable one during the positioning. Additionally, ensemble algorithms such as AdaBoost and Bagging are applied to improve the decision tree classifier performance nearly same as k-NN that is resulted as the best classifier for indoor positioning.

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“…When K = 1, the unknown tuple is assigned the class of the 1 training tuple that is closest in the pattern space. This method is also called the lazy learner method as it simply stores the training data and waits until it is given with test data [2].…”

Section: K-nearest Neighbourmentioning

confidence: 99%

“…Random forest is often used when we have large training data sets and large number of input variables. At the end, this method builds many decision trees [2].…”

Section: Literature Review 21 Decision Treementioning

confidence: 99%

“…It is an extraction of implicit but previously undiscovered or unknown knowledge from data in databases [1]. Different classification techniques are used for data mining purpose [2]. Classification and association are the two popular techniques used to predict user interest and relationship between those data items which has been used by users [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Usage of Classification based Association for Removal of Noisy Attributes

Yadav¹,

Rajeswari²,

Vaithiyanathan³

2014

IJCA

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Data mining is a process of extracting knowledge from underlying huge multidimensional data. Data mining techniques discovers hidden patterns from a given data. Classification is one of the techniques of data mining. Data Classification consists of categorization of data under the known class labels for its most effective and efficient utilization. There are different algorithms available for classification. Association rule mining is used to generate the rules for strongly associated attributes. It helps to uncover the association between seemingly unrelated attributes. Association rules are identified by analyzing patterns which satisfies the confidence and support criteria. This paper explains pre-processing for a given bank data set along with classification and association rule mining. Association followed by classification method helps in finding the noisy data attributes. Experimental setup uses WEKA tool for data mining. WEKA is a collection of machine learning algorithms for data mining tasks. Experiment has shown that classification with guidance of strongly supported rules from association rule mining helps in removal of noise and has increased the accuracy of classifier for a given data set.

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Comparison of data mining classification algorithms for breast cancer prediction

Cited by 73 publications

References 3 publications

An Efficient Approach towards Crimes against Women using Time Series Algorithm

An Efficient Approach towards Crimes against Women using Time Series Algorithm

A comparative study on machine learning algorithms for indoor positioning

Usage of Classification based Association for Removal of Noisy Attributes

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