Two-stage feature selection using ranking self-adaptive differential   evolution algorithm for recognition of acceleration activity

MUHAMMAD NOORAZLAN SHAH ZAINUDIN; Md Nasir Bin SULAIMAN; NORWATI MUSTAPHA; THINAGARAN PERUMAL; Raihani MOHAMED

Makale detayları

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

Yazarlar:

MUHAMMAD NOORAZLAN SHAH ZAINUDIN | Md Nasir Bin SULAIMAN | NORWATI MUSTAPHA | THINAGARAN PERUMAL | Raihani MOHAMED

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 3, pp.1378-1389

Belge Tipi:

Makale

Yayın Tarihi:

5/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000735106

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Widespread research on activity recognition is becoming   an imperative topic for improving the quality of human health. The fast   development of sensing technology has become a fundamental platform for   researchers to implement a system that could fulfill human needs. Due to   privacy interests and low cost, wearable sensing technology is used in   numerous physical activity monitoring and recognition systems. While these   systems have proved to be successful, it is crucial to pay attention to the   less relevant features to be classified. In such circumstances, it might   happen that some features are less meaningful for describing the activity.   Less complex and easy to understand, feature ranking is gaining a lot of   attention in most feature dimension problems such as in bioinformatics and   hyperspectral images. However, the improvement of ranking features in   activity recognition has not yet been achieved. On the other hand, an   evolutionary algorithm has proven its effectiveness in searching the best   feature subsets. An exhaustive searching process of finding an optimal   parameter value is another challenge. Consequently, this paper proposes a   ranking self-adaptive differential evolution (rsaDE) feature selection   algorithm. The proposed algorithm is capable of selecting the optimal   feature subsets while improving the recognition of acceleration activity   using a minimum number of features. The experiments employed real-world   physical acceleration data sets: WISDM and PAMAP2. As a result, rsaDE   performed better than the current methods in terms of model performance and   its efficiency in the context of random forest ensemble classifiers.

Makale detayları

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

Yazarlar: MUHAMMAD NOORAZLAN SHAH ZAINUDIN | Md Nasir Bin SULAIMAN | NORWATI MUSTAPHA | THINAGARAN PERUMAL | Raihani MOHAMED

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 3, pp.1378-1389

Belge Tipi: Makale

Yayın Tarihi: 5/2018

Belge Dili: Türkçe

Document ID (Org No.): 2000735106

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

MUHAMMAD NOORAZLAN SHAH ZAINUDIN | Md Nasir Bin SULAIMAN | NORWATI MUSTAPHA | THINAGARAN PERUMAL | Raihani MOHAMED

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 3, pp.1378-1389

Belge Tipi:

Makale

Yayın Tarihi:

5/2018

Belge Dili:

Türkçe

Document ID (Org No.):

2000735106

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632