Limitations of Machine Learning

 

Along with the opportunities and benefits that machine learning systems provide, there are also limitations and challenges. These limitations can be explained as follows:

Determining the appropriate algorithm for the learning problem is one of the most important problems for machine learning. Researchers need to be able to determine the algorithm for the needs of the problem and test many different algorithms for this. Along with the algorithm, model parameters should also be determined. While some algorithms perform well for text processing, different algorithms for image processing may perform better.

The noise contained in the available data is another limitation of machine learning. The presence of structured and / or unstructured data at the same time between data heaps, which arise especially with the concept of big data, is another problem to be tackled in machine learning. Noise in data; Differences in the characteristics of an image such as size, color, resolution can appear in different ways such as misspellings, punctuation marks, special symbols and abbreviations used in a text data.

Feature extraction is one of the most important steps of the machine learning system as it changes depending on the correct operation of the system and the selection of the correct features and number of features. The feature extraction process depends on the problem with which the transaction is performed and is specific. To be more precise, the characteristics determined in a health problem will be different from the features that will be used for an autonomous vehicle. The extraction of a common feature produced for different problem solutions in different disciplines may benefit very large problem solutions in the future.

Over learning is another machine learning limitation. While the model created shows high performance during training, it may underperform or underperform than expected on test data. In this case, it is thought that the training data are memorized by the model, in other words, the model is over-learned. To prevent this, the complexity of the model is increased during the training and various methods are tried.

In controlled machine learning methods, the model is trained on the labeled data and the detection and diagnosis are performed. In order to construct this model, a considerable amount of large data should be available. For such a data labeling job, experts and hard work are required. It is also possible to cause human-induced errors. There may also be a lack of expert personnel in solving special problems. All of these come together to reveal the limitation of data labeling.

REFERENCE

Savaş, S. (2019), Karotis Arter Intima Media Kalınlığının Derin Öğrenme ile Sınıflandırılması, Gazi Üniversitesi Fen Bilimleri Enstitüsü Bilgisayar Mühendisliği Ana Bilim Dalı, Doktora Tezi, Ankara.