A Review on Advanced Decision Trees for Efficient & Effective k-NN Classification

K Nearest Neighbor (KNN) strategy is a notable classification strategy in data mining and estimations in light of its direct execution and colossal arrangement execution. In any case, it is outlandish for ordinary KNN strategies to select settled k esteem to all tests. Past courses of action assign different k esteems to different test tests by the cross endorsement strategy however are typically tedious. This work proposes new KNN strategies, first is a KTree strategy to learn unique k esteems for different test or new cases, by including a training arrange in the KNN classification. This work additionally proposes a change rendition of KTree technique called K*Tree to speed its test organize by putting additional data of the training tests in the leaf node of KTree, for example, the training tests situated in the leaf node, their KNNs, and the closest neighbor of these KNNs. K*Tree, which empowers to lead KNN arrangement utilizing a subset of the training tests in the leaf node instead of all training tests utilized in the recently KNN techniques. This really reduces the cost of test organize

EEG-Based Biometric Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

This paper proposes an Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre-processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K-Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model

Belirsizlik Koşularında Fuzzy Rough Algoritması: Kredi Skorlama’da Bir Uygulama

Günümüzün artan rekabetçi ortamı, bankaların tüketicilerin artan kredi taleplerine çabuk ve hızlı karar vermelerini gerektirmektedir. Bu amaçla bankalar müşterilere kredi verirken karar vermelerine yardımcı olan istatistik ya da makina öğrenmesi tabanlı kredi skorlama modelleri kullanmaktadırlar. Çalışmada kredi skorlama modellerindeki özellikle belirsizlik konusundaki eksikliği gidermek için bulanık-kaba küme tabanlı bir kredi skorlama modeli önerilmektedir. Bulanık ve kaba kümeler teoremine dayanan yöntem veri kümesindeki örneklerin bulanıklık benzerliklerini hesaplayarak tüketicinin kredi almaya olan uygunluğunu belirleyen kararlar vermektedir. Model sonuçları, yaygın olarak kullanılan diğer kredi skorlama yöntemleriyle karşılaştırılmış ve önerdiğimiz kredi skorlama modellerinden daha iyi olduğunu göstermiştir

Belirsizlik Koşularında Fuzzy Rough Algoritması: Kredi Skorlama’da Bir Uygulama

Günümüzün artan rekabetçi ortamı, bankaların tüketicilerin artan kredi taleplerine çabuk ve hızlı karar vermelerini gerektirmektedir. Bu amaçla bankalar müşterilere kredi verirken karar vermelerine yardımcı olan istatistik ya da makina öğrenmesi tabanlı kredi skorlama modelleri kullanmaktadırlar. Çalışmada kredi skorlama modellerindeki özellikle belirsizlik konusundaki eksikliği gidermek için bulanık-kaba küme tabanlı bir kredi skorlama modeli önerilmektedir. Bulanık ve kaba kümeler teoremine dayanan yöntem veri kümesindeki örneklerin bulanıklık benzerliklerini hesaplayarak tüketicinin kredi almaya olan uygunluğunu belirleyen kararlar vermektedir. Model sonuçları, yaygın olarak kullanılan diğer kredi skorlama yöntemleriyle karşılaştırılmış ve önerdiğimiz kredi skorlama modellerinden daha iyi olduğunu göstermiştir

An Empirical investigation into software effort estimation by analogy

Most practitioners recognise the important part accurate estimates of development effort play in the successful management of major software projects. However, it is widely recognised that current estimation techniques are often very inaccurate, while studies (Heemstra 1992; Lederer and Prasad 1993) have shown that effort estimation research is not being effectively transferred from the research domain into practical application. Traditionally, research has been almost exclusively focused on the advancement of algorithmic models (e.g. COCOMO (Boehm 1981) and SLIM (Putnam 1978)), where effort is commonly expressed as a function of system size. However, in recent years there has been a discernible movement away from algorithmic models with non-algorithmic systems (often encompassing machine learning facets) being actively researched. This is potentially a very exciting and important time in this field, with new approaches regularly being proposed. One such technique, estimation by analogy, is the focus of this thesis. The principle behind estimation by analogy is that past experience can often provide insights and solutions to present problems. Software projects are characterised in terms of collectable features (such as the number of screens or the size of the functional requirements) and stored in a historical case base as they are completed. Once a case base of sufficient size has been cultivated, new projects can be estimated by finding similar historical projects and re-using the recorded effort. To make estimation by analogy feasible it became necessary to construct a software tool, dubbed ANGEL, which allowed the collection of historical project data and the generation of estimates for new software projects. A substantial empirical validation of the approach was made encompassing approximately 250 real historical software projects across eight industrial data sets, using stepwise regression as a benchmark. Significance tests on the results accepted the hypothesis (at the 1% confidence level) that estimation by analogy is a superior prediction system to stepwise regression in terms of accuracy. A study was also made of the sensitivity of the analogy approach. By growing project data sets in a pseudo time-series fashion it was possible to answer pertinent questions about the approach, such as, what are the effects of outlying projects and what is the minimum data set size? The main conclusions of this work are that estimation by analogy is a viable estimation technique that would seem to offer some advantages over algorithmic approaches including, improved accuracy, easier use of categorical features and an ability to operate even where no statistical relationships can be found