Data Mining Using $\mathcal{MLC}++$ a Machine Learning Library in C++

Kohavi, Ron; Sommerfield, Dan; Dougherty, James

doi:10.1142/s021821309700027x

Cited by 136 publications

(92 citation statements)

References 6 publications

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“…The landmarking meta-features extracted from the meta-data of these dataset. Also the Naive Bayes [17], IBK [18] , J48 [19], AdaBoost , LogitBoost [20], PART [21], RandomForest [22], Bagging [23] and SMO [24], classifier are apply on given dataset and accuracy of each classifier is calculated. This is knowledge base of system.…”

Section: System Outlinementioning

confidence: 99%

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Algorithm Selection based on Landmarking Meta-feature

Balte¹,

Pise²,

Agrawal³

2015

CAE

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Knowledge discovery is the data mining task. Number of classification algorithms is present for knowledge discovery task in data mining. Each algorithm is differentiating with another based on their performance. No free lunch theorem [1] states that there no single prediction of algorithm is not possible for all kind of datasets. This implies that performance value of algorithm changes according to dataset characteristics. Non-expert can't understand which will be best classifier for his/her dataset. Meta-learning is one machine learning technique which supports non-expert users for selecting classifier. In meta learning dataset characteristics well know as meta-features. Based on these meta-features the prediction of well suitable classifier is done. In this paper, in the first experiment, the prediction classifier is done by landmarking meta-features with k-NN approach. In the second experiment in addition to first experiment Win/ draw/ loss of corresponding classifiers is calculated using recommendation method and based on that the best classifier is recommended. Here the simple linear regression value of classifiers is taken into consideration. In both the experiments performance measure is the accuracy of classifier.

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Section: System Outlinementioning

confidence: 99%

“…Where, the neighbor selection [17] algorithm is used with traditional distance formula. The distance of new dataset from the old dataset is calculated by, distance = ∑(new meta-feature)-∑(old meta-feature).…”

Section: Experiment-1mentioning

confidence: 99%

Algorithm Selection based on Landmarking Meta-feature

Balte¹,

Pise²,

Agrawal³

2015

CAE

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“…• Validación cruzada: se ha realizado una validación cruzada de k partes [14] donde k = 10. De esta manera, nuestro conjunto de datos se divide 10 veces en 10 partes diferentes.…”

Section: Metodología De Experimentaciónunclassified

Modelo predictivo de control en fundiciones de alta precisión: un nuevo enfoque para la fase de predicción

Nieves¹,

Santos²,

Bringas³

2011

REVMETAL

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Se tiene conocimiento que desde épocas ancestrales existen los procesos productivos. Procesos que han quedado representados tanto en versos bíblicos y pinturas egipcias, como en los grabados de las vasijas griegas. A lo largo de todo este tiempo, los procesos productivos han permitido transformar las materias primas en productos elaborados. Antiguamente, el proceso era mucho más primitivo, sin embargo, en la actualidad, y debido al avance y evolución de la sociedad, el proceso se ha mejorado. Los cambios más relevantes que se han producido a lo largo de las últi-mas décadas son: (i) la competencia ha aumentado drásticamente y (ii) las limitaciones impuestas por la legislación medioambiental han dotado de una complejidad extra al proceso de producción.Estos cambios implican que se lleve a cabo un control estricto de los procesos de producción. De esta forma, y gracias a la ayuda de múltiples disciplinas del conocimiento, es posible anticiparse y solucionar las situaciones adversas del proceso productivo. Así, los aspectos fundamentales sobre los que se puede trabajar son los siguientes: (i) minimizar los costes, (ii) maximizar la tasa de producción, (iii) Palabras claveModelo predictivo de control; Aprendizaje automático; Predicción de defectos; Minería de datos; Optimización de procesos. Model predictive control on high precision foundries: a new approach for the prediction phase AbstractA Model Predictive Control (MPC) is a system which allows us to control a production plant. Thanks to this type of system is possible to make a production that comes close to "zero defects". In order to achieve its main goal, this kind of systems consists of several phases. One of the most important is the phase that predicts the situation in which the plant is going to be in a given time. Currently, the majority of the research in this field are related to linear MPC, although the process, which the model tries to represent, may not be. Thus, this paper presents several experiments that proof that the forecast phase, usually represented by a single mathematical function, can be represented by machine-learning models. KeywordsModel predictive control; Machine learning; Fault prediction; Data mining; Process optimisation. minimizar el stock, (iv) controlar la fluctuación de los precios y (v) evitar fallos en el servicio del producto debidos a defectos ocultos. Esta situación también está afectando al proceso de producción de piezas de metal o proceso metalúrgico. Esta industria es una parte importante de la evolución de nuestra sociedad. Ya desde el punto de vista histórico se ha relacionado el proceso de producción metalúrgico con el desarrollo de la civilización humana, considerándolo incluso como una de las profesiones más antiguas de la historia. La fundición provee a otras industrias, como a la industria automovilística o naval, entre otras. Debido a la naturaleza de estas industrias, hay que realizar grandes controles de seguridad que aseguren el funcionamiento final de las piezas producidas, ya que el menor de los defecto...

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“…For each domain, we induced classifiers for the minority class (for Road, we chose the class Grass). We selected several induction algorithms from MLC++ (Kohavi, Sommerfield, & Dougherty, 1997): a decision tree learner (MC4), Naive Bayes with discretization (NB), k-nearest neighbor for several k values (IBk), and Bagged-MC4 (Breiman, 1996). MC4 is similar to C4.5 (Quinlan, 1993); probabilistic predictions are made by using a Laplace correction at the leaves.…”

Section: Our Studymentioning

confidence: 99%

Untitled

2001

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Abstract. In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. In some cases, the performance of the hybrid actually can surpass that of the best known classifier. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. The hybrid also is efficient to build, to store, and to update. The hybrid is based on a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull (ROCCH) method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.

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Data Mining Using $\mathcal{MLC}++$ a Machine Learning Library in C++

Cited by 136 publications

References 6 publications

Algorithm Selection based on Landmarking Meta-feature

Algorithm Selection based on Landmarking Meta-feature

Modelo predictivo de control en fundiciones de alta precisión: un nuevo enfoque para la fase de predicción

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