Machine learning: a review of classification and combining techniques

Kotsiantis, Sotiris; Zaharakis, Ioannis D.; Pintelas, Panayiotis E.

doi:10.1007/s10462-007-9052-3

Cited by 2,676 publications

(2,645 citation statements)

References 109 publications

Supporting

Mentioning

2,547

Contrasting

Unclassified

Order By: Relevance

“…Each node in a decision tree represents a feature in a case to be classified, and each branch represents a value that the node can assume. Cases are classified starting at the root node and sorted based on their feature values [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification

Aleskandarany

Soria

Green

et al. 2015

Breast Cancer Res Treat

View full text Add to dashboard Cite

Accurate distant metastasis (DM) prediction is critical for risk stratification and effective treatment decisions in breast cancer (BC). Many prognostic markers/models based on tissue marker studies are continually emerging using conventional statistical approaches analysing complex/dimensional data association with DM/poor prognosis. However, few of them have fulfilled satisfactory evidences for clinical application. This study aimed at building DM risk assessment algorithm for BC patients.A well-characterised series of early invasive primary operable BC (n=1902), with immunohistochemical (IHC) expression of a panel of biomarkers (n=31) formed the material of this study. Decision tree algorithm was computed using WEKA software, utilising quantitative biomarkers' expression and the absence/presence of distant metastases.Fifteen biomarkers were significantly associated with DM, with six temporal subgroups characterised based on time-to-development of DM ranging from < 1 year to > 15 years of follow-up. Of these 15 biomarkers, 10 had a significant expression pattern where Ki67LI, HER2, p53, N-cadherin, P-cadherin, PIK3CA and TOMM34 showed significantly higher expressions with earlier development of DM. In contrast, higher expressions of ER, PR, and BCL2, were associated with delayed occurrence of DM. DM prediction algorithm was built utilising cases informative for the 15 significant markers. Four risk groups of patients were characterised. Three markers; p53, HER2 and BCL2 predicted the probability of DM, based on software-generated cut-offs, with a precision rate of 81.1% for positive predictive value and 77.3%, for the negative predictive value.This algorithm reiterates the reported prognostic values of these three markers and underscores their central biologic role in BC progression. Further independent validation of this pruned panel of biomarkers is therefore warranted.3

show abstract

Section: Introductionmentioning

confidence: 99%

“…These outputs are then expressed as models, in the form of decision trees or sets of if-then rules, which can be used to classify new cases, with an emphasis on making the models understandable as well as accurate. In general, it is often possible to prune a decision tree to obtain a simpler and more accurate tree [6,8].…”

Section: Introductionmentioning

confidence: 99%

Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification

Aleskandarany

Soria

Green

et al. 2015

Breast Cancer Res Treat

View full text Add to dashboard Cite

show abstract

“…We have also set the dimension of word vectors 9 as 500 in W2V model and used with default parameters. To validate our predictive model, we have used n-fold cross-validation (CV) technique which is most commonly used method in machine learning [41]. This method randomly divides dataset n times into n complementary subsets.…”

Section: Configurationmentioning

confidence: 99%

Turkish Music Genre Classification using Audio and Lyrics Features

Çoban¹

2017

SDÜ Fen Bil Enst Der

View full text Add to dashboard Cite

Music Information Retrieval (MIR) has become a popular research area in recent years. In this context, researchers have developed music information systems to find solutions for such major problems as automatic playlist creation, hit song detection, and music genre or mood classification. Meta-data information, lyrics, or melodic content of music are used as feature resource in previous works. However, lyrics do not often used in MIR systems and the number of works in this field is not enough especially for Turkish. In this paper, firstly, we have extended our previously created Turkish MIR (TMIR) dataset, which comprises of Turkish lyrics, by including the audio file of each song. Secondly, we have investigated the effect of using audio and textual features together or separately on automatic Music Genre Classification (MGC). We have extracted textual features from lyrics using different feature extraction models such as word2vec and traditional Bag of Words. We have conducted our experiments on Support Vector Machine (SVM) algorithm and analysed the impact of feature selection and different feature groups on MGC. We have considered lyrics based MGC as a text classification task and also investigated the effect of term weighting method. Experimental results show that textual features can also be effective as well as audio features for Turkish MGC, especially when a supervised term weighting method is employed. We have achieved the highest success rate as 99,12% by using both audio and textual features together.

show abstract

“…Instances are classified starting from the root node and following the argument down based on their attribute values until it reaches the final node. Generally, decision trees can easily translated into a particular set of rules, where each standalone path from the root node to a certain leaf node can produce a particular rule [22,24]. The decision tree algorithm is a well-known classification technique that used in several problem domains, including medical diagnosis, classification problems, marketing, fraud detection and so on [23].…”

Section: Decision Tree Classifiermentioning

confidence: 99%

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Aljaaf

Al‐Jumeily

Hussain

et al. 2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

View full text Add to dashboard Cite

Abstract-Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy.

show abstract

Machine learning: a review of classification and combining techniques

Cited by 2,676 publications

References 109 publications

Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification

Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification

Turkish Music Genre Classification using Audio and Lyrics Features

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Contact Info

Product

Resources

About