Fuzzy Aggregation Operators With Application in the Energy Performance of Buildings

Abbaszadeh, Sadegh; Tavakoli, Alireza; Movahedan, Marjan; Лю, Пэйдэ

doi:10.1615/int.j.uncertaintyquantification.2018024815

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our current approach, the transformations f i are determined in an unsupervised manner, i.e., using the feature information x (1) , . .…”

Section: Feature Transformationmentioning

confidence: 99%

“…Examples of such combinations include methods for learning the majority rule model [17], the non-compensatory sorting model [18], or the Choquet integral [21]. In contrast to many other machine learning approaches, corresponding models are interpretable and meaningful from of decision making point of view, a property that has gained increasing attention in the recent past [6,1]. Besides, they often guarantee other properties that might be desirable, such as monotonicity.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning With the Sugeno Integral: The Case of Binary Classification

Abbaszadeh

Hüllermeier

2021

IEEE Trans. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

In this paper, we elaborate on the use of the Sugeno integral in the context of machine learning. More specifically, we propose a method for binary classification, in which the Sugeno integral is used as an aggregation function that combines several local evaluations of an instance, pertaining to different features or measurements, into a single global evaluation. Due to the specific nature of the Sugeno integral, this approach is especially suitable for learning from ordinal data, that is, when measurements are taken from ordinal scales. This is a topic that has not received much attention in machine learning so far. The core of the learning problem itself consists of identifying the capacity underlying the Sugeno integral. To tackle this problem, we develop an algorithm based on linear programming. The algorithm also includes a suitable technique for transforming the original feature values into local evaluations (local utility scores), as well as a method for tuning a threshold on the global evaluation. To control the flexibility of the classifier and mitigate the problem of overfitting the training data, we generalize our approach toward k-maxitive capacities, where k plays the role of a hyper-parameter of the learner. We present experimental studies, in which we compare our method with competing approaches on several benchmark data sets.

show abstract

“…In our current approach, the transformations f i are determined in an unsupervised manner, i.e., using the feature information x (1) , . .…”

Section: Feature Transformationmentioning

confidence: 99%